{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Setting environment and dataset files\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import environ\n",
    "environ['train_device'] = 'cuda:1' # training device: 'cpu' or 'cuda:X'\n",
    "environ['store_device'] = 'cuda:1' # Data storing device:  'cpu' or 'cuda:X'\n",
    "\n",
    "dataset_file_MC = '/data/lm4718/datasets/dataset_multicomp_fusion_train_val+batch375001-499939.pkl'\n",
    "test_dataset_file_MC = '/data/kk4796/datasets/dataset_multicomp_fusion_test.pkl'\n",
    "benchmark_dataset_file_MC ='/data/mm12191/datasets/benchmarks_ds2.json' \n",
    "\n",
    "# train_val_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_train_val.pkl'\n",
    "# test_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_test.pkl'\n",
    "\n",
    "train_val_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_train_val.pkl'\n",
    "test_dataset_file_SC = '/data/kk4796/datasets/dataset_singlecomp_13M115_test.pkl'\n",
    "\n",
    "\n",
    "%run utils_k-best.py # imports and defines some utils functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Data\n",
    "<a id='load_data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 196/196 [00:04<00:00, 42.50it/s]\n",
      "100%|██████████| 14607/14607 [00:19<00:00, 756.69it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 22\n",
      "Number of batches dropped due to too much memory accesses:642\n",
      "Data loaded\n",
      "Sizes: (22, 0) batches\n"
     ]
    }
   ],
   "source": [
    "#Loading the test dataset\n",
    "test_dataset, test_bl, test_indices, _, _ = load_merge_data(test_dataset_file_MC, test_dataset_file_SC, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28443/28443 [00:28<00:00, 1013.72it/s]\n",
      "100%|██████████| 131466/131466 [03:05<00:00, 707.38it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 356\n",
      "Number of batches dropped due to too much memory accesses:7448\n",
      "Data loaded\n",
      "Sizes: (71, 285) batches\n"
     ]
    }
   ],
   "source": [
    "#Loading the train/val dataset\n",
    "train_val_dataset, val_bl, val_indices, train_bl, train_indices = load_merge_data(dataset_file_MC, train_val_dataset_file_SC, 0.2, max_batch_size=832, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total batches number\n",
    "len(train_val_dataset.X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([54, 2, 1272]) <BL0<BL1<BL2[CI0CI1]EL2>EL1>EL0>\n",
      "torch.Size([28, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3[CI1]EL3>EL1>EL0>\n",
      "torch.Size([4, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4[CI1]EL4>EL2>EL1>EL0>\n",
      "torch.Size([83, 2, 1272]) <BL0<BL1[CI0CI1]EL1>EL0>\n",
      "torch.Size([5, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4[CI1]EL4>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3><BL4<BL5[CI1]EL5>EL4>EL2>EL1>EL0>\n",
      "torch.Size([3, 2, 1272]) <BL0<BL1<BL2<BL3[CI0CI1]EL3>EL2>EL1>EL0>\n",
      "torch.Size([7, 2, 1272]) <BL0<BL1<BL2[CI0]EL2><BL3<BL4[CI1]EL4>EL3>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0CI1]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3><BL5[CI1]EL5>EL2>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2><BL5[CI1]EL5>EL1>EL0>\n",
      "torch.Size([1, 2, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2><BL4<BL5<BL6[CI1]EL6>EL5>EL4>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1878, 1, 1272]) <BL0<BL1<BL2[CI0]EL2>EL1>EL0>\n",
      "torch.Size([1406, 1, 1272]) <BL0<BL1<BL2<BL3[CI0]EL3>EL2>EL1>EL0>\n",
      "torch.Size([2048, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([304, 1, 1272]) <BL0<BL1[CI0]EL1>EL0>\n",
      "torch.Size([139, 1, 1272]) <BL0<BL1<BL2<BL3<BL4[CI0]EL4>EL3>EL2>EL1>EL0>\n",
      "torch.Size([5, 1, 1272]) <BL0<BL1<BL2<BL3<BL4<BL5[CI0]EL5>EL4>EL3>EL2>EL1>EL0>\n"
     ]
    }
   ],
   "source": [
    "#batches sizes and tree structure\n",
    "for i in range(len(test_dataset.X)):\n",
    "    print(test_dataset.X[i][1].size(), get_tree_footprint(test_dataset.X[i][0]) )  \n",
    "    #format of first output: batch size, number of computations, size of each comps vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %run utils_k-best.py\n",
    "\n",
    "input_size = 1272\n",
    "\n",
    "model = None \n",
    "\n",
    "model = Model_Recursive_LSTM_v2(input_size,comp_embed_layer_sizes=[600, 900, 600, 400, 200], drops=[0.275, 0.4, 0.275, 0.175, 0.175], output_size=106 * 5)\n",
    "\n",
    "model.to(train_device) \n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "optimizer = AdamW(model.parameters(),weight_decay=0.375e-2)  #0.375e-2\n",
    "# optimizer = optim.RMSprop(model.parameters(), weight_decay=0.3e-2) # Other tested optimizer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000:  train Loss: 1322.7775   val Loss: 960.1473   time: 7.57s   best: 960.1473\n",
      "Epoch 2/2000:  train Loss: 753.0821   val Loss: 821.7638   time: 3.20s   best: 821.7638\n",
      "Epoch 3/2000:  train Loss: 679.9027   val Loss: 779.0272   time: 3.20s   best: 779.0272\n",
      "Epoch 4/2000:  train Loss: 638.6730   val Loss: 696.6442   time: 3.22s   best: 696.6442\n",
      "Epoch 5/2000:  train Loss: 608.2019   val Loss: 645.3721   time: 3.26s   best: 645.3721\n",
      "Epoch 6/2000:  train Loss: 579.5869   val Loss: 617.8765   time: 3.22s   best: 617.8765\n",
      "Epoch 7/2000:  train Loss: 556.8300   val Loss: 599.8071   time: 3.20s   best: 599.8071\n",
      "Epoch 8/2000:  train Loss: 540.3982   val Loss: 573.3190   time: 3.22s   best: 573.3190\n",
      "Epoch 9/2000:  train Loss: 522.6018   val Loss: 563.9882   time: 3.20s   best: 563.9882\n",
      "Epoch 10/2000:  train Loss: 513.2949   val Loss: 548.7905   time: 3.21s   best: 548.7905\n",
      "Epoch 11/2000:  train Loss: 501.3183   val Loss: 541.7233   time: 3.33s   best: 541.7233\n",
      "Epoch 12/2000:  train Loss: 494.0130   val Loss: 536.7237   time: 3.50s   best: 536.7237\n",
      "Epoch 13/2000:  train Loss: 487.3597   val Loss: 527.7069   time: 3.55s   best: 527.7069\n",
      "Epoch 14/2000:  train Loss: 483.2931   val Loss: 526.8275   time: 3.41s   best: 526.8275\n",
      "Epoch 15/2000:  train Loss: 476.1343   val Loss: 515.4880   time: 3.42s   best: 515.4880\n",
      "Epoch 16/2000:  train Loss: 467.8389   val Loss: 517.2145   time: 3.43s   best: 515.4880\n",
      "Epoch 17/2000:  train Loss: 466.0340   val Loss: 507.0838   time: 3.43s   best: 507.0838\n",
      "Epoch 18/2000:  train Loss: 459.3170   val Loss: 513.3856   time: 3.43s   best: 507.0838\n",
      "Epoch 19/2000:  train Loss: 456.0947   val Loss: 499.1372   time: 3.44s   best: 499.1372\n",
      "Epoch 20/2000:  train Loss: 455.6573   val Loss: 505.0304   time: 3.44s   best: 499.1372\n",
      "Epoch 21/2000:  train Loss: 449.3573   val Loss: 500.6736   time: 3.42s   best: 499.1372\n",
      "Epoch 22/2000:  train Loss: 446.3901   val Loss: 491.9838   time: 3.54s   best: 491.9838\n",
      "Epoch 23/2000:  train Loss: 444.4627   val Loss: 491.9705   time: 3.92s   best: 491.9705\n",
      "Epoch 24/2000:  train Loss: 441.2799   val Loss: 486.7564   time: 3.50s   best: 486.7564\n",
      "Epoch 25/2000:  train Loss: 438.0442   val Loss: 481.1340   time: 3.49s   best: 481.1340\n",
      "Epoch 26/2000:  train Loss: 436.2336   val Loss: 480.0657   time: 3.45s   best: 480.0657\n",
      "Epoch 27/2000:  train Loss: 432.5735   val Loss: 473.1302   time: 3.26s   best: 473.1302\n",
      "Epoch 28/2000:  train Loss: 430.4710   val Loss: 464.8470   time: 3.39s   best: 464.8470\n",
      "Epoch 29/2000:  train Loss: 424.9284   val Loss: 467.5397   time: 3.32s   best: 464.8470\n",
      "Epoch 30/2000:  train Loss: 423.7667   val Loss: 461.1956   time: 3.16s   best: 461.1956\n",
      "Epoch 31/2000:  train Loss: 418.9129   val Loss: 484.6399   time: 3.14s   best: 461.1956\n",
      "Epoch 32/2000:  train Loss: 420.6948   val Loss: 461.3250   time: 3.53s   best: 461.1956\n",
      "Epoch 33/2000:  train Loss: 417.4097   val Loss: 456.9140   time: 3.61s   best: 456.9140\n",
      "Epoch 34/2000:  train Loss: 413.9586   val Loss: 456.7748   time: 3.39s   best: 456.7748\n",
      "Epoch 35/2000:  train Loss: 411.2126   val Loss: 451.0430   time: 3.49s   best: 451.0430\n",
      "Epoch 36/2000:  train Loss: 407.5477   val Loss: 458.0768   time: 3.34s   best: 451.0430\n",
      "Epoch 37/2000:  train Loss: 408.4545   val Loss: 443.7463   time: 3.14s   best: 443.7463\n",
      "Epoch 38/2000:  train Loss: 405.7739   val Loss: 463.1477   time: 3.07s   best: 443.7463\n",
      "Epoch 39/2000:  train Loss: 399.3069   val Loss: 465.0858   time: 3.07s   best: 443.7463\n",
      "Epoch 40/2000:  train Loss: 402.3762   val Loss: 469.7808   time: 3.26s   best: 443.7463\n",
      "Epoch 41/2000:  train Loss: 401.0409   val Loss: 438.7236   time: 3.44s   best: 438.7236\n",
      "Epoch 42/2000:  train Loss: 392.4805   val Loss: 443.8727   time: 3.59s   best: 438.7236\n",
      "Epoch 43/2000:  train Loss: 391.6097   val Loss: 440.7772   time: 3.46s   best: 438.7236\n",
      "Epoch 44/2000:  train Loss: 389.9000   val Loss: 438.9387   time: 3.44s   best: 438.7236\n",
      "Epoch 45/2000:  train Loss: 390.0300   val Loss: 445.7594   time: 3.42s   best: 438.7236\n",
      "Epoch 46/2000:  train Loss: 396.8365   val Loss: 435.6122   time: 3.42s   best: 435.6122\n",
      "Epoch 47/2000:  train Loss: 389.9371   val Loss: 458.5462   time: 3.46s   best: 435.6122\n",
      "Epoch 48/2000:  train Loss: 387.0400   val Loss: 427.2410   time: 3.54s   best: 427.2410\n",
      "Epoch 49/2000:  train Loss: 381.5322   val Loss: 428.6538   time: 3.44s   best: 427.2410\n",
      "Epoch 50/2000:  train Loss: 381.5039   val Loss: 434.9450   time: 3.43s   best: 427.2410\n",
      "Epoch 51/2000:  train Loss: 380.7835   val Loss: 456.3746   time: 3.43s   best: 427.2410\n",
      "Epoch 52/2000:  train Loss: 382.0330   val Loss: 423.7430   time: 3.42s   best: 423.7430\n",
      "Epoch 53/2000:  train Loss: 378.4869   val Loss: 434.5993   time: 3.43s   best: 423.7430\n",
      "Epoch 54/2000:  train Loss: 377.1777   val Loss: 437.7972   time: 3.43s   best: 423.7430\n",
      "Epoch 55/2000:  train Loss: 375.7644   val Loss: 427.2932   time: 3.56s   best: 423.7430\n",
      "Epoch 56/2000:  train Loss: 376.6749   val Loss: 474.8602   time: 3.47s   best: 423.7430\n",
      "Epoch 57/2000:  train Loss: 378.9236   val Loss: 427.6741   time: 3.44s   best: 423.7430\n",
      "Epoch 58/2000:  train Loss: 372.9927   val Loss: 429.7045   time: 3.44s   best: 423.7430\n",
      "Epoch 59/2000:  train Loss: 371.3793   val Loss: 418.0868   time: 3.44s   best: 418.0868\n",
      "Epoch 60/2000:  train Loss: 370.1956   val Loss: 415.1199   time: 3.43s   best: 415.1199\n",
      "Epoch 61/2000:  train Loss: 367.9980   val Loss: 426.5507   time: 3.43s   best: 415.1199\n",
      "Epoch 62/2000:  train Loss: 372.7794   val Loss: 450.6443   time: 3.55s   best: 415.1199\n",
      "Epoch 63/2000:  train Loss: 369.7155   val Loss: 415.4741   time: 3.49s   best: 415.1199\n",
      "Epoch 64/2000:  train Loss: 366.5483   val Loss: 434.4822   time: 3.43s   best: 415.1199\n",
      "Epoch 65/2000:  train Loss: 366.0201   val Loss: 419.8115   time: 3.44s   best: 415.1199\n",
      "Epoch 66/2000:  train Loss: 366.0750   val Loss: 416.7829   time: 3.42s   best: 415.1199\n",
      "Epoch 67/2000:  train Loss: 364.7162   val Loss: 412.1356   time: 3.42s   best: 412.1356\n",
      "Epoch 68/2000:  train Loss: 363.4456   val Loss: 408.7644   time: 3.42s   best: 408.7644\n",
      "Epoch 69/2000:  train Loss: 363.5888   val Loss: 413.6105   time: 3.53s   best: 408.7644\n",
      "Epoch 70/2000:  train Loss: 364.4512   val Loss: 438.6184   time: 3.50s   best: 408.7644\n",
      "Epoch 71/2000:  train Loss: 364.9033   val Loss: 428.1856   time: 3.43s   best: 408.7644\n",
      "Epoch 72/2000:  train Loss: 363.9148   val Loss: 427.6471   time: 3.43s   best: 408.7644\n",
      "Epoch 73/2000:  train Loss: 363.8516   val Loss: 472.4115   time: 3.42s   best: 408.7644\n",
      "Epoch 74/2000:  train Loss: 374.0619   val Loss: 439.3997   time: 3.42s   best: 408.7644\n",
      "Epoch 75/2000:  train Loss: 362.6636   val Loss: 410.8012   time: 3.41s   best: 408.7644\n",
      "Epoch 76/2000:  train Loss: 359.4846   val Loss: 440.7570   time: 3.45s   best: 408.7644\n",
      "Epoch 77/2000:  train Loss: 366.8653   val Loss: 476.8106   time: 3.56s   best: 408.7644\n",
      "Epoch 78/2000:  train Loss: 363.7759   val Loss: 409.7663   time: 3.44s   best: 408.7644\n",
      "Epoch 79/2000:  train Loss: 361.0276   val Loss: 440.5336   time: 3.43s   best: 408.7644\n",
      "Epoch 80/2000:  train Loss: 363.4928   val Loss: 441.9208   time: 3.43s   best: 408.7644\n",
      "Epoch 81/2000:  train Loss: 364.2921   val Loss: 432.0148   time: 3.42s   best: 408.7644\n",
      "Epoch 82/2000:  train Loss: 360.0267   val Loss: 409.8704   time: 3.32s   best: 408.7644\n",
      "Epoch 83/2000:  train Loss: 359.7708   val Loss: 455.0979   time: 3.25s   best: 408.7644\n",
      "Epoch 84/2000:  train Loss: 362.0197   val Loss: 425.3441   time: 3.28s   best: 408.7644\n",
      "Epoch 85/2000:  train Loss: 356.7744   val Loss: 419.7241   time: 3.24s   best: 408.7644\n",
      "Epoch 86/2000:  train Loss: 367.8068   val Loss: 423.1276   time: 3.22s   best: 408.7644\n",
      "Epoch 87/2000:  train Loss: 358.5345   val Loss: 402.7893   time: 3.26s   best: 402.7893\n",
      "Epoch 88/2000:  train Loss: 355.5410   val Loss: 423.3821   time: 3.20s   best: 402.7893\n",
      "Epoch 89/2000:  train Loss: 355.6335   val Loss: 409.3000   time: 3.07s   best: 402.7893\n",
      "Epoch 90/2000:  train Loss: 353.4534   val Loss: 413.2724   time: 3.07s   best: 402.7893\n",
      "Epoch 91/2000:  train Loss: 354.3766   val Loss: 431.7239   time: 3.07s   best: 402.7893\n",
      "Epoch 92/2000:  train Loss: 359.1896   val Loss: 470.1318   time: 3.06s   best: 402.7893\n",
      "Epoch 93/2000:  train Loss: 362.7003   val Loss: 427.2806   time: 3.06s   best: 402.7893\n",
      "Epoch 94/2000:  train Loss: 353.7441   val Loss: 400.5934   time: 3.10s   best: 400.5934\n",
      "Epoch 95/2000:  train Loss: 350.4814   val Loss: 399.6311   time: 3.16s   best: 399.6311\n",
      "Epoch 96/2000:  train Loss: 349.2961   val Loss: 399.4517   time: 3.08s   best: 399.4517\n",
      "Epoch 97/2000:  train Loss: 349.1241   val Loss: 399.4482   time: 3.07s   best: 399.4482\n",
      "Epoch 98/2000:  train Loss: 352.6833   val Loss: 412.8478   time: 3.07s   best: 399.4482\n",
      "Epoch 99/2000:  train Loss: 350.8412   val Loss: 469.0290   time: 3.07s   best: 399.4482\n",
      "Epoch 100/2000:  train Loss: 355.4947   val Loss: 407.6198   time: 3.07s   best: 399.4482\n",
      "Epoch 101/2000:  train Loss: 350.7059   val Loss: 442.7471   time: 3.07s   best: 399.4482\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 102/2000:  train Loss: 350.0593   val Loss: 413.8966   time: 3.14s   best: 399.4482\n",
      "Epoch 103/2000:  train Loss: 347.4743   val Loss: 401.6790   time: 3.16s   best: 399.4482\n",
      "Epoch 104/2000:  train Loss: 346.4279   val Loss: 400.5598   time: 3.08s   best: 399.4482\n",
      "Epoch 105/2000:  train Loss: 345.9710   val Loss: 413.1387   time: 3.07s   best: 399.4482\n",
      "Epoch 106/2000:  train Loss: 344.1111   val Loss: 394.0479   time: 3.07s   best: 394.0479\n",
      "Epoch 107/2000:  train Loss: 345.3883   val Loss: 394.5640   time: 3.06s   best: 394.0479\n",
      "Epoch 108/2000:  train Loss: 341.8367   val Loss: 389.4060   time: 3.07s   best: 389.4060\n",
      "Epoch 109/2000:  train Loss: 343.3317   val Loss: 412.1581   time: 3.07s   best: 389.4060\n",
      "Epoch 110/2000:  train Loss: 342.5123   val Loss: 388.2998   time: 3.15s   best: 388.2998\n",
      "Epoch 111/2000:  train Loss: 345.5206   val Loss: 389.1159   time: 3.14s   best: 388.2998\n",
      "Epoch 112/2000:  train Loss: 340.7766   val Loss: 392.2703   time: 3.08s   best: 388.2998\n",
      "Epoch 113/2000:  train Loss: 338.4179   val Loss: 383.2735   time: 3.08s   best: 383.2735\n",
      "Epoch 114/2000:  train Loss: 338.0795   val Loss: 391.8457   time: 3.08s   best: 383.2735\n",
      "Epoch 115/2000:  train Loss: 338.0283   val Loss: 386.1619   time: 3.07s   best: 383.2735\n",
      "Epoch 116/2000:  train Loss: 336.5994   val Loss: 393.9401   time: 3.07s   best: 383.2735\n",
      "Epoch 117/2000:  train Loss: 336.8330   val Loss: 408.1020   time: 3.08s   best: 383.2735\n",
      "Epoch 118/2000:  train Loss: 335.1363   val Loss: 400.9156   time: 3.16s   best: 383.2735\n",
      "Epoch 119/2000:  train Loss: 334.7429   val Loss: 383.7941   time: 3.12s   best: 383.2735\n",
      "Epoch 120/2000:  train Loss: 338.7706   val Loss: 454.3651   time: 3.08s   best: 383.2735\n",
      "Epoch 121/2000:  train Loss: 342.5994   val Loss: 382.9522   time: 3.08s   best: 382.9522\n",
      "Epoch 122/2000:  train Loss: 329.1391   val Loss: 370.7538   time: 3.08s   best: 370.7538\n",
      "Epoch 123/2000:  train Loss: 328.1704   val Loss: 370.3549   time: 3.07s   best: 370.3549\n",
      "Epoch 124/2000:  train Loss: 325.2801   val Loss: 377.0630   time: 3.07s   best: 370.3549\n",
      "Epoch 125/2000:  train Loss: 326.3877   val Loss: 382.2866   time: 3.09s   best: 370.3549\n",
      "Epoch 126/2000:  train Loss: 329.0352   val Loss: 362.6887   time: 3.17s   best: 362.6887\n",
      "Epoch 127/2000:  train Loss: 322.9833   val Loss: 368.8340   time: 3.11s   best: 362.6887\n",
      "Epoch 128/2000:  train Loss: 321.7664   val Loss: 374.7228   time: 3.08s   best: 362.6887\n",
      "Epoch 129/2000:  train Loss: 320.3482   val Loss: 363.1190   time: 3.08s   best: 362.6887\n",
      "Epoch 130/2000:  train Loss: 321.5993   val Loss: 362.6640   time: 3.08s   best: 362.6640\n",
      "Epoch 131/2000:  train Loss: 321.6184   val Loss: 370.8572   time: 3.07s   best: 362.6640\n",
      "Epoch 132/2000:  train Loss: 324.1583   val Loss: 386.7995   time: 3.07s   best: 362.6640\n",
      "Epoch 133/2000:  train Loss: 317.2122   val Loss: 354.5703   time: 3.10s   best: 354.5703\n",
      "Epoch 134/2000:  train Loss: 314.9551   val Loss: 354.7154   time: 3.18s   best: 354.5703\n",
      "Epoch 135/2000:  train Loss: 313.5768   val Loss: 360.1326   time: 3.09s   best: 354.5703\n",
      "Epoch 136/2000:  train Loss: 312.4590   val Loss: 351.8938   time: 3.08s   best: 351.8938\n",
      "Epoch 137/2000:  train Loss: 311.5584   val Loss: 349.9453   time: 3.08s   best: 349.9453\n",
      "Epoch 138/2000:  train Loss: 310.4885   val Loss: 357.9647   time: 3.08s   best: 349.9453\n",
      "Epoch 139/2000:  train Loss: 311.0833   val Loss: 361.5031   time: 3.07s   best: 349.9453\n",
      "Epoch 140/2000:  train Loss: 310.4646   val Loss: 352.2244   time: 3.07s   best: 349.9453\n",
      "Epoch 141/2000:  train Loss: 315.4033   val Loss: 400.7133   time: 3.12s   best: 349.9453\n",
      "Epoch 142/2000:  train Loss: 312.9228   val Loss: 348.3715   time: 3.16s   best: 348.3715\n",
      "Epoch 143/2000:  train Loss: 305.4377   val Loss: 357.0361   time: 3.09s   best: 348.3715\n",
      "Epoch 144/2000:  train Loss: 308.3214   val Loss: 347.0976   time: 3.08s   best: 347.0976\n",
      "Epoch 145/2000:  train Loss: 305.0525   val Loss: 352.1388   time: 3.08s   best: 347.0976\n",
      "Epoch 146/2000:  train Loss: 307.8098   val Loss: 346.1540   time: 3.08s   best: 346.1540\n",
      "Epoch 147/2000:  train Loss: 304.0207   val Loss: 347.8420   time: 3.08s   best: 346.1540\n",
      "Epoch 148/2000:  train Loss: 301.4275   val Loss: 345.3227   time: 3.09s   best: 345.3227\n",
      "Epoch 149/2000:  train Loss: 300.9287   val Loss: 346.1843   time: 3.16s   best: 345.3227\n",
      "Epoch 150/2000:  train Loss: 303.2855   val Loss: 346.3423   time: 3.15s   best: 345.3227\n",
      "Epoch 151/2000:  train Loss: 300.3577   val Loss: 344.5282   time: 3.09s   best: 344.5282\n",
      "Epoch 152/2000:  train Loss: 304.3293   val Loss: 349.1009   time: 3.09s   best: 344.5282\n",
      "Epoch 153/2000:  train Loss: 298.8186   val Loss: 341.0047   time: 3.09s   best: 341.0047\n",
      "Epoch 154/2000:  train Loss: 298.1523   val Loss: 355.8747   time: 3.09s   best: 341.0047\n",
      "Epoch 155/2000:  train Loss: 299.2450   val Loss: 340.7834   time: 3.09s   best: 340.7834\n",
      "Epoch 156/2000:  train Loss: 297.2545   val Loss: 341.8895   time: 3.10s   best: 340.7834\n",
      "Epoch 157/2000:  train Loss: 299.1777   val Loss: 351.2480   time: 3.18s   best: 340.7834\n",
      "Epoch 158/2000:  train Loss: 299.1364   val Loss: 374.2032   time: 3.13s   best: 340.7834\n",
      "Epoch 159/2000:  train Loss: 300.3615   val Loss: 338.3890   time: 3.09s   best: 338.3890\n",
      "Epoch 160/2000:  train Loss: 294.1075   val Loss: 339.5071   time: 3.09s   best: 338.3890\n",
      "Epoch 161/2000:  train Loss: 293.9233   val Loss: 338.6646   time: 3.09s   best: 338.3890\n",
      "Epoch 162/2000:  train Loss: 292.9139   val Loss: 335.7486   time: 3.08s   best: 335.7486\n",
      "Epoch 163/2000:  train Loss: 293.3939   val Loss: 337.8822   time: 3.08s   best: 335.7486\n",
      "Epoch 164/2000:  train Loss: 293.7953   val Loss: 335.5933   time: 3.11s   best: 335.5933\n",
      "Epoch 165/2000:  train Loss: 303.4460   val Loss: 367.3906   time: 3.19s   best: 335.5933\n",
      "Epoch 166/2000:  train Loss: 294.7690   val Loss: 334.0502   time: 3.11s   best: 334.0502\n",
      "Epoch 167/2000:  train Loss: 289.2756   val Loss: 329.2913   time: 3.10s   best: 329.2913\n",
      "Epoch 168/2000:  train Loss: 289.4796   val Loss: 344.4384   time: 3.10s   best: 329.2913\n",
      "Epoch 169/2000:  train Loss: 289.5161   val Loss: 329.7268   time: 3.10s   best: 329.2913\n",
      "Epoch 170/2000:  train Loss: 289.5206   val Loss: 329.4956   time: 3.10s   best: 329.2913\n",
      "Epoch 171/2000:  train Loss: 289.8612   val Loss: 337.5556   time: 3.10s   best: 329.2913\n",
      "Epoch 172/2000:  train Loss: 289.2568   val Loss: 329.7725   time: 3.16s   best: 329.2913\n",
      "Epoch 173/2000:  train Loss: 287.7115   val Loss: 331.1172   time: 3.18s   best: 329.2913\n",
      "Epoch 174/2000:  train Loss: 288.2481   val Loss: 337.2127   time: 3.10s   best: 329.2913\n",
      "Epoch 175/2000:  train Loss: 296.2286   val Loss: 350.3186   time: 3.10s   best: 329.2913\n",
      "Epoch 176/2000:  train Loss: 289.8479   val Loss: 337.8711   time: 3.10s   best: 329.2913\n",
      "Epoch 177/2000:  train Loss: 289.2158   val Loss: 327.0489   time: 3.10s   best: 327.0489\n",
      "Epoch 178/2000:  train Loss: 288.9696   val Loss: 384.2643   time: 3.10s   best: 327.0489\n",
      "Epoch 179/2000:  train Loss: 292.0011   val Loss: 326.7292   time: 3.10s   best: 326.7292\n",
      "Epoch 180/2000:  train Loss: 285.9797   val Loss: 327.0011   time: 3.19s   best: 326.7292\n",
      "Epoch 181/2000:  train Loss: 283.6190   val Loss: 327.0611   time: 3.14s   best: 326.7292\n",
      "Epoch 182/2000:  train Loss: 282.3936   val Loss: 322.1634   time: 3.10s   best: 322.1634\n",
      "Epoch 183/2000:  train Loss: 283.1958   val Loss: 324.3830   time: 3.10s   best: 322.1634\n",
      "Epoch 184/2000:  train Loss: 282.8617   val Loss: 321.8938   time: 3.10s   best: 321.8938\n",
      "Epoch 185/2000:  train Loss: 281.8070   val Loss: 324.1615   time: 3.10s   best: 321.8938\n",
      "Epoch 186/2000:  train Loss: 289.5242   val Loss: 361.9357   time: 3.09s   best: 321.8938\n",
      "Epoch 187/2000:  train Loss: 288.0239   val Loss: 325.9682   time: 3.12s   best: 321.8938\n",
      "Epoch 188/2000:  train Loss: 280.9918   val Loss: 325.6044   time: 3.19s   best: 321.8938\n",
      "Epoch 189/2000:  train Loss: 279.9029   val Loss: 318.8229   time: 3.13s   best: 318.8229\n",
      "Epoch 190/2000:  train Loss: 279.7486   val Loss: 327.2116   time: 3.10s   best: 318.8229\n",
      "Epoch 191/2000:  train Loss: 286.6502   val Loss: 393.5938   time: 3.10s   best: 318.8229\n",
      "Epoch 192/2000:  train Loss: 286.5140   val Loss: 325.1932   time: 3.10s   best: 318.8229\n",
      "Epoch 193/2000:  train Loss: 280.0938   val Loss: 332.4144   time: 3.09s   best: 318.8229\n",
      "Epoch 194/2000:  train Loss: 280.5445   val Loss: 323.7702   time: 3.10s   best: 318.8229\n",
      "Epoch 195/2000:  train Loss: 285.6922   val Loss: 342.6734   time: 3.14s   best: 318.8229\n",
      "Epoch 196/2000:  train Loss: 280.0021   val Loss: 320.1512   time: 3.19s   best: 318.8229\n",
      "Epoch 197/2000:  train Loss: 277.9745   val Loss: 321.6060   time: 3.11s   best: 318.8229\n",
      "Epoch 198/2000:  train Loss: 280.2338   val Loss: 333.3811   time: 3.10s   best: 318.8229\n",
      "Epoch 199/2000:  train Loss: 279.6823   val Loss: 315.7838   time: 3.09s   best: 315.7838\n",
      "Epoch 200/2000:  train Loss: 279.0816   val Loss: 320.8541   time: 3.16s   best: 315.7838\n",
      "Epoch 201/2000:  train Loss: 277.5092   val Loss: 315.9172   time: 3.15s   best: 315.7838\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 202/2000:  train Loss: 276.0375   val Loss: 315.5157   time: 3.21s   best: 315.5157\n",
      "Epoch 203/2000:  train Loss: 277.4270   val Loss: 317.3421   time: 3.27s   best: 315.5157\n",
      "Epoch 204/2000:  train Loss: 275.3052   val Loss: 327.9568   time: 3.30s   best: 315.5157\n",
      "Epoch 205/2000:  train Loss: 279.6721   val Loss: 368.7615   time: 3.29s   best: 315.5157\n",
      "Epoch 206/2000:  train Loss: 286.1139   val Loss: 314.5051   time: 3.27s   best: 314.5051\n",
      "Epoch 207/2000:  train Loss: 274.8598   val Loss: 318.4208   time: 3.33s   best: 314.5051\n",
      "Epoch 208/2000:  train Loss: 274.6668   val Loss: 318.1782   time: 3.43s   best: 314.5051\n",
      "Epoch 209/2000:  train Loss: 275.4463   val Loss: 318.6487   time: 3.33s   best: 314.5051\n",
      "Epoch 210/2000:  train Loss: 277.1831   val Loss: 313.4042   time: 3.30s   best: 313.4042\n",
      "Epoch 211/2000:  train Loss: 275.7210   val Loss: 316.4450   time: 3.31s   best: 313.4042\n",
      "Epoch 212/2000:  train Loss: 274.4680   val Loss: 314.8847   time: 3.29s   best: 313.4042\n",
      "Epoch 213/2000:  train Loss: 273.7903   val Loss: 317.0452   time: 3.28s   best: 313.4042\n",
      "Epoch 214/2000:  train Loss: 274.8496   val Loss: 322.5911   time: 3.66s   best: 313.4042\n",
      "Epoch 215/2000:  train Loss: 275.9799   val Loss: 314.6764   time: 3.56s   best: 313.4042\n",
      "Epoch 216/2000:  train Loss: 275.4681   val Loss: 318.0986   time: 3.30s   best: 313.4042\n",
      "Epoch 217/2000:  train Loss: 273.3193   val Loss: 314.2615   time: 3.29s   best: 313.4042\n",
      "Epoch 218/2000:  train Loss: 273.0310   val Loss: 314.5441   time: 3.29s   best: 313.4042\n",
      "Epoch 219/2000:  train Loss: 271.3961   val Loss: 313.1756   time: 3.29s   best: 313.1756\n",
      "Epoch 220/2000:  train Loss: 273.6247   val Loss: 312.8823   time: 3.28s   best: 312.8823\n",
      "Epoch 221/2000:  train Loss: 274.3452   val Loss: 311.0055   time: 3.36s   best: 311.0055\n",
      "Epoch 222/2000:  train Loss: 270.9771   val Loss: 311.1471   time: 3.39s   best: 311.0055\n",
      "Epoch 223/2000:  train Loss: 273.2641   val Loss: 317.2747   time: 3.29s   best: 311.0055\n",
      "Epoch 224/2000:  train Loss: 271.2438   val Loss: 312.7797   time: 3.29s   best: 311.0055\n",
      "Epoch 225/2000:  train Loss: 271.9684   val Loss: 309.9446   time: 3.29s   best: 309.9446\n",
      "Epoch 226/2000:  train Loss: 269.7119   val Loss: 307.5764   time: 3.28s   best: 307.5764\n",
      "Epoch 227/2000:  train Loss: 273.9536   val Loss: 307.5948   time: 3.28s   best: 307.5764\n",
      "Epoch 228/2000:  train Loss: 269.9585   val Loss: 310.6521   time: 3.32s   best: 307.5764\n",
      "Epoch 229/2000:  train Loss: 269.0130   val Loss: 306.2209   time: 3.40s   best: 306.2209\n",
      "Epoch 230/2000:  train Loss: 275.8437   val Loss: 306.8704   time: 3.31s   best: 306.2209\n",
      "Epoch 231/2000:  train Loss: 268.4254   val Loss: 305.2751   time: 3.29s   best: 305.2751\n",
      "Epoch 232/2000:  train Loss: 269.0446   val Loss: 314.1380   time: 3.29s   best: 305.2751\n",
      "Epoch 233/2000:  train Loss: 267.8453   val Loss: 307.1614   time: 3.29s   best: 305.2751\n",
      "Epoch 234/2000:  train Loss: 268.8044   val Loss: 308.8536   time: 3.28s   best: 305.2751\n",
      "Epoch 235/2000:  train Loss: 269.0512   val Loss: 308.6647   time: 3.28s   best: 305.2751\n",
      "Epoch 236/2000:  train Loss: 268.0117   val Loss: 311.9703   time: 3.39s   best: 305.2751\n",
      "Epoch 237/2000:  train Loss: 269.6386   val Loss: 306.1500   time: 3.35s   best: 305.2751\n",
      "Epoch 238/2000:  train Loss: 266.8933   val Loss: 304.1689   time: 3.29s   best: 304.1689\n",
      "Epoch 239/2000:  train Loss: 266.2969   val Loss: 303.1826   time: 3.29s   best: 303.1826\n",
      "Epoch 240/2000:  train Loss: 267.1914   val Loss: 308.4928   time: 3.29s   best: 303.1826\n",
      "Epoch 241/2000:  train Loss: 266.8651   val Loss: 310.5530   time: 3.28s   best: 303.1826\n",
      "Epoch 242/2000:  train Loss: 266.7208   val Loss: 301.6002   time: 3.28s   best: 301.6002\n",
      "Epoch 243/2000:  train Loss: 270.4973   val Loss: 305.4040   time: 3.32s   best: 301.6002\n",
      "Epoch 244/2000:  train Loss: 266.6240   val Loss: 310.5904   time: 3.39s   best: 301.6002\n",
      "Epoch 245/2000:  train Loss: 266.5382   val Loss: 302.9811   time: 3.29s   best: 301.6002\n",
      "Epoch 246/2000:  train Loss: 267.9209   val Loss: 315.1625   time: 3.26s   best: 301.6002\n",
      "Epoch 247/2000:  train Loss: 268.1771   val Loss: 301.8491   time: 3.09s   best: 301.6002\n",
      "Epoch 248/2000:  train Loss: 264.4676   val Loss: 301.3317   time: 3.08s   best: 301.3317\n",
      "Epoch 249/2000:  train Loss: 263.8416   val Loss: 308.6434   time: 3.17s   best: 301.3317\n",
      "Epoch 250/2000:  train Loss: 267.6856   val Loss: 304.4178   time: 3.15s   best: 301.3317\n",
      "Epoch 251/2000:  train Loss: 264.2928   val Loss: 303.1781   time: 3.10s   best: 301.3317\n",
      "Epoch 252/2000:  train Loss: 264.2682   val Loss: 304.3130   time: 3.09s   best: 301.3317\n",
      "Epoch 253/2000:  train Loss: 266.8735   val Loss: 314.8253   time: 3.10s   best: 301.3317\n",
      "Epoch 254/2000:  train Loss: 272.5827   val Loss: 330.2611   time: 3.10s   best: 301.3317\n",
      "Epoch 255/2000:  train Loss: 265.2392   val Loss: 300.1206   time: 3.18s   best: 300.1206\n",
      "Epoch 256/2000:  train Loss: 264.0369   val Loss: 304.0131   time: 3.17s   best: 300.1206\n",
      "Epoch 257/2000:  train Loss: 268.5072   val Loss: 313.1754   time: 3.11s   best: 300.1206\n",
      "Epoch 258/2000:  train Loss: 264.6761   val Loss: 306.5434   time: 3.11s   best: 300.1206\n",
      "Epoch 259/2000:  train Loss: 269.9080   val Loss: 297.2417   time: 3.11s   best: 297.2417\n",
      "Epoch 260/2000:  train Loss: 261.0248   val Loss: 297.0894   time: 3.10s   best: 297.0894\n",
      "Epoch 261/2000:  train Loss: 261.1894   val Loss: 300.1357   time: 3.10s   best: 297.0894\n",
      "Epoch 262/2000:  train Loss: 262.1478   val Loss: 305.6768   time: 3.10s   best: 297.0894\n",
      "Epoch 263/2000:  train Loss: 262.0278   val Loss: 298.8544   time: 3.19s   best: 297.0894\n",
      "Epoch 264/2000:  train Loss: 262.5399   val Loss: 302.4351   time: 3.16s   best: 297.0894\n",
      "Epoch 265/2000:  train Loss: 261.2475   val Loss: 295.7738   time: 3.11s   best: 295.7738\n",
      "Epoch 266/2000:  train Loss: 260.5234   val Loss: 301.7148   time: 3.11s   best: 295.7738\n",
      "Epoch 267/2000:  train Loss: 260.9840   val Loss: 296.9209   time: 3.10s   best: 295.7738\n",
      "Epoch 268/2000:  train Loss: 261.4601   val Loss: 300.2295   time: 3.10s   best: 295.7738\n",
      "Epoch 269/2000:  train Loss: 261.8755   val Loss: 303.2067   time: 3.09s   best: 295.7738\n",
      "Epoch 270/2000:  train Loss: 260.1933   val Loss: 294.5296   time: 3.14s   best: 294.5296\n",
      "Epoch 271/2000:  train Loss: 261.9916   val Loss: 308.3505   time: 3.20s   best: 294.5296\n",
      "Epoch 272/2000:  train Loss: 261.7285   val Loss: 297.7717   time: 3.12s   best: 294.5296\n",
      "Epoch 273/2000:  train Loss: 259.5663   val Loss: 295.3975   time: 3.11s   best: 294.5296\n",
      "Epoch 274/2000:  train Loss: 259.1153   val Loss: 298.9759   time: 3.11s   best: 294.5296\n",
      "Epoch 275/2000:  train Loss: 259.8943   val Loss: 294.6149   time: 3.10s   best: 294.5296\n",
      "Epoch 276/2000:  train Loss: 260.5153   val Loss: 295.4507   time: 3.10s   best: 294.5296\n",
      "Epoch 277/2000:  train Loss: 260.7100   val Loss: 294.1104   time: 3.10s   best: 294.1104\n",
      "Epoch 278/2000:  train Loss: 258.3990   val Loss: 299.0833   time: 3.15s   best: 294.1104\n",
      "Epoch 279/2000:  train Loss: 256.8983   val Loss: 298.8623   time: 3.19s   best: 294.1104\n",
      "Epoch 280/2000:  train Loss: 260.7019   val Loss: 298.0722   time: 3.11s   best: 294.1104\n",
      "Epoch 281/2000:  train Loss: 257.6720   val Loss: 294.6092   time: 3.11s   best: 294.1104\n",
      "Epoch 282/2000:  train Loss: 259.8203   val Loss: 299.3347   time: 3.11s   best: 294.1104\n",
      "Epoch 283/2000:  train Loss: 259.5255   val Loss: 297.0780   time: 3.10s   best: 294.1104\n",
      "Epoch 284/2000:  train Loss: 257.1332   val Loss: 297.6133   time: 3.10s   best: 294.1104\n",
      "Epoch 285/2000:  train Loss: 257.3565   val Loss: 295.3723   time: 3.11s   best: 294.1104\n",
      "Epoch 286/2000:  train Loss: 258.6101   val Loss: 293.4997   time: 3.19s   best: 293.4997\n",
      "Epoch 287/2000:  train Loss: 258.2999   val Loss: 294.3557   time: 3.15s   best: 293.4997\n",
      "Epoch 288/2000:  train Loss: 255.2759   val Loss: 290.9975   time: 3.11s   best: 290.9975\n",
      "Epoch 289/2000:  train Loss: 257.4566   val Loss: 294.7784   time: 3.11s   best: 290.9975\n",
      "Epoch 290/2000:  train Loss: 255.7167   val Loss: 293.0773   time: 3.10s   best: 290.9975\n",
      "Epoch 291/2000:  train Loss: 256.8450   val Loss: 296.7362   time: 3.10s   best: 290.9975\n",
      "Epoch 292/2000:  train Loss: 256.4701   val Loss: 296.4767   time: 3.10s   best: 290.9975\n",
      "Epoch 293/2000:  train Loss: 257.8496   val Loss: 292.3098   time: 3.13s   best: 290.9975\n",
      "Epoch 294/2000:  train Loss: 256.8208   val Loss: 287.2497   time: 3.20s   best: 287.2497\n",
      "Epoch 295/2000:  train Loss: 255.8871   val Loss: 286.3254   time: 3.12s   best: 286.3254\n",
      "Epoch 296/2000:  train Loss: 254.0187   val Loss: 292.4585   time: 3.11s   best: 286.3254\n",
      "Epoch 297/2000:  train Loss: 255.6432   val Loss: 289.8893   time: 3.11s   best: 286.3254\n",
      "Epoch 298/2000:  train Loss: 254.3448   val Loss: 293.4338   time: 3.10s   best: 286.3254\n",
      "Epoch 299/2000:  train Loss: 254.8394   val Loss: 291.0479   time: 3.10s   best: 286.3254\n",
      "Epoch 300/2000:  train Loss: 253.7324   val Loss: 287.4410   time: 3.09s   best: 286.3254\n",
      "Epoch 301/2000:  train Loss: 253.6532   val Loss: 292.8413   time: 3.17s   best: 286.3254\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 302/2000:  train Loss: 253.7926   val Loss: 286.8022   time: 3.17s   best: 286.3254\n",
      "Epoch 303/2000:  train Loss: 254.0462   val Loss: 293.9072   time: 3.09s   best: 286.3254\n",
      "Epoch 304/2000:  train Loss: 254.4074   val Loss: 289.9066   time: 3.09s   best: 286.3254\n",
      "Epoch 305/2000:  train Loss: 255.7671   val Loss: 290.3168   time: 3.09s   best: 286.3254\n",
      "Epoch 306/2000:  train Loss: 254.9335   val Loss: 287.4360   time: 3.07s   best: 286.3254\n",
      "Epoch 307/2000:  train Loss: 257.3693   val Loss: 289.1011   time: 3.09s   best: 286.3254\n",
      "Epoch 308/2000:  train Loss: 251.8469   val Loss: 284.9237   time: 3.10s   best: 284.9237\n",
      "Epoch 309/2000:  train Loss: 251.4562   val Loss: 287.3288   time: 3.18s   best: 284.9237\n",
      "Epoch 310/2000:  train Loss: 256.3112   val Loss: 285.8454   time: 3.14s   best: 284.9237\n",
      "Epoch 311/2000:  train Loss: 252.9315   val Loss: 288.1018   time: 3.10s   best: 284.9237\n",
      "Epoch 312/2000:  train Loss: 252.2751   val Loss: 292.1394   time: 3.10s   best: 284.9237\n",
      "Epoch 313/2000:  train Loss: 252.2025   val Loss: 287.5086   time: 3.09s   best: 284.9237\n",
      "Epoch 314/2000:  train Loss: 254.2924   val Loss: 283.4894   time: 3.09s   best: 283.4894\n",
      "Epoch 315/2000:  train Loss: 251.2672   val Loss: 285.2436   time: 3.08s   best: 283.4894\n",
      "Epoch 316/2000:  train Loss: 249.5848   val Loss: 282.5559   time: 3.10s   best: 282.5559\n",
      "Epoch 317/2000:  train Loss: 250.5705   val Loss: 286.3355   time: 3.20s   best: 282.5559\n",
      "Epoch 318/2000:  train Loss: 251.9426   val Loss: 287.1859   time: 3.11s   best: 282.5559\n",
      "Epoch 319/2000:  train Loss: 251.5949   val Loss: 285.3217   time: 3.10s   best: 282.5559\n",
      "Epoch 320/2000:  train Loss: 251.6404   val Loss: 295.1795   time: 3.10s   best: 282.5559\n",
      "Epoch 321/2000:  train Loss: 254.5871   val Loss: 285.9929   time: 3.09s   best: 282.5559\n",
      "Epoch 322/2000:  train Loss: 249.5523   val Loss: 282.0840   time: 3.09s   best: 282.0840\n",
      "Epoch 323/2000:  train Loss: 248.5026   val Loss: 284.7961   time: 3.08s   best: 282.0840\n",
      "Epoch 324/2000:  train Loss: 250.0611   val Loss: 288.1068   time: 3.10s   best: 282.0840\n",
      "Epoch 325/2000:  train Loss: 249.6327   val Loss: 281.7488   time: 3.19s   best: 281.7488\n",
      "Epoch 326/2000:  train Loss: 250.1216   val Loss: 294.0404   time: 3.11s   best: 281.7488\n",
      "Epoch 327/2000:  train Loss: 251.3189   val Loss: 283.2957   time: 3.10s   best: 281.7488\n",
      "Epoch 328/2000:  train Loss: 249.7957   val Loss: 282.4355   time: 3.10s   best: 281.7488\n",
      "Epoch 329/2000:  train Loss: 249.0973   val Loss: 285.2359   time: 3.09s   best: 281.7488\n",
      "Epoch 330/2000:  train Loss: 250.5642   val Loss: 293.4689   time: 3.09s   best: 281.7488\n",
      "Epoch 331/2000:  train Loss: 251.7636   val Loss: 286.6442   time: 3.08s   best: 281.7488\n",
      "Epoch 332/2000:  train Loss: 248.3630   val Loss: 280.8112   time: 3.14s   best: 280.8112\n",
      "Epoch 333/2000:  train Loss: 246.9988   val Loss: 282.9614   time: 3.18s   best: 280.8112\n",
      "Epoch 334/2000:  train Loss: 249.8858   val Loss: 283.6649   time: 3.10s   best: 280.8112\n",
      "Epoch 335/2000:  train Loss: 247.9196   val Loss: 283.7203   time: 3.09s   best: 280.8112\n",
      "Epoch 336/2000:  train Loss: 246.8723   val Loss: 281.0203   time: 3.10s   best: 280.8112\n",
      "Epoch 337/2000:  train Loss: 250.0688   val Loss: 281.4401   time: 3.09s   best: 280.8112\n",
      "Epoch 338/2000:  train Loss: 248.0248   val Loss: 278.3987   time: 3.09s   best: 278.3987\n",
      "Epoch 339/2000:  train Loss: 246.4176   val Loss: 278.2694   time: 3.09s   best: 278.2694\n",
      "Epoch 340/2000:  train Loss: 244.1979   val Loss: 280.8673   time: 3.15s   best: 278.2694\n",
      "Epoch 341/2000:  train Loss: 246.9547   val Loss: 280.1033   time: 3.17s   best: 278.2694\n",
      "Epoch 342/2000:  train Loss: 248.7178   val Loss: 287.9761   time: 3.10s   best: 278.2694\n",
      "Epoch 343/2000:  train Loss: 246.4192   val Loss: 279.5932   time: 3.10s   best: 278.2694\n",
      "Epoch 344/2000:  train Loss: 246.7641   val Loss: 281.2514   time: 3.10s   best: 278.2694\n",
      "Epoch 345/2000:  train Loss: 247.3335   val Loss: 282.9213   time: 3.09s   best: 278.2694\n",
      "Epoch 346/2000:  train Loss: 244.2882   val Loss: 278.7105   time: 3.09s   best: 278.2694\n",
      "Epoch 347/2000:  train Loss: 253.4914   val Loss: 277.4343   time: 3.09s   best: 277.4343\n",
      "Epoch 348/2000:  train Loss: 245.1206   val Loss: 274.0991   time: 3.20s   best: 274.0991\n",
      "Epoch 349/2000:  train Loss: 242.7967   val Loss: 274.6846   time: 3.12s   best: 274.0991\n",
      "Epoch 350/2000:  train Loss: 244.1720   val Loss: 275.8472   time: 3.10s   best: 274.0991\n",
      "Epoch 351/2000:  train Loss: 243.2188   val Loss: 278.1466   time: 3.10s   best: 274.0991\n",
      "Epoch 352/2000:  train Loss: 245.4421   val Loss: 280.2771   time: 3.09s   best: 274.0991\n",
      "Epoch 353/2000:  train Loss: 243.7627   val Loss: 279.2059   time: 3.09s   best: 274.0991\n",
      "Epoch 354/2000:  train Loss: 243.2533   val Loss: 275.8293   time: 3.07s   best: 274.0991\n",
      "Epoch 355/2000:  train Loss: 243.5221   val Loss: 277.7103   time: 3.11s   best: 274.0991\n",
      "Epoch 356/2000:  train Loss: 242.8241   val Loss: 278.9039   time: 3.17s   best: 274.0991\n",
      "Epoch 357/2000:  train Loss: 244.8480   val Loss: 280.0559   time: 3.11s   best: 274.0991\n",
      "Epoch 358/2000:  train Loss: 249.7437   val Loss: 289.8076   time: 3.08s   best: 274.0991\n",
      "Epoch 359/2000:  train Loss: 250.2544   val Loss: 274.1584   time: 3.17s   best: 274.0991\n",
      "Epoch 360/2000:  train Loss: 243.5560   val Loss: 273.0743   time: 3.21s   best: 273.0743\n",
      "Epoch 361/2000:  train Loss: 241.9845   val Loss: 274.2328   time: 3.16s   best: 273.0743\n",
      "Epoch 362/2000:  train Loss: 243.3367   val Loss: 276.6616   time: 3.08s   best: 273.0743\n",
      "Epoch 363/2000:  train Loss: 241.9507   val Loss: 273.1155   time: 3.15s   best: 273.0743\n",
      "Epoch 364/2000:  train Loss: 241.3345   val Loss: 276.3691   time: 3.17s   best: 273.0743\n",
      "Epoch 365/2000:  train Loss: 243.6312   val Loss: 279.7828   time: 3.10s   best: 273.0743\n",
      "Epoch 366/2000:  train Loss: 245.1131   val Loss: 278.1725   time: 3.09s   best: 273.0743\n",
      "Epoch 367/2000:  train Loss: 244.3796   val Loss: 280.9646   time: 3.10s   best: 273.0743\n",
      "Epoch 368/2000:  train Loss: 241.8193   val Loss: 270.8942   time: 3.09s   best: 270.8942\n",
      "Epoch 369/2000:  train Loss: 242.9418   val Loss: 275.0585   time: 3.09s   best: 270.8942\n",
      "Epoch 370/2000:  train Loss: 243.9504   val Loss: 304.1785   time: 3.09s   best: 270.8942\n",
      "Epoch 371/2000:  train Loss: 244.9443   val Loss: 274.9381   time: 3.17s   best: 270.8942\n",
      "Epoch 372/2000:  train Loss: 242.5454   val Loss: 269.9379   time: 3.15s   best: 269.9379\n",
      "Epoch 373/2000:  train Loss: 240.0438   val Loss: 276.8340   time: 3.10s   best: 269.9379\n",
      "Epoch 374/2000:  train Loss: 240.5183   val Loss: 270.5801   time: 3.10s   best: 269.9379\n",
      "Epoch 375/2000:  train Loss: 241.3670   val Loss: 270.9446   time: 3.10s   best: 269.9379\n",
      "Epoch 376/2000:  train Loss: 238.3505   val Loss: 270.2521   time: 3.09s   best: 269.9379\n",
      "Epoch 377/2000:  train Loss: 239.4037   val Loss: 270.5890   time: 3.09s   best: 269.9379\n",
      "Epoch 378/2000:  train Loss: 246.5253   val Loss: 299.6075   time: 3.09s   best: 269.9379\n",
      "Epoch 379/2000:  train Loss: 245.6905   val Loss: 270.1381   time: 3.17s   best: 269.9379\n",
      "Epoch 380/2000:  train Loss: 239.9203   val Loss: 270.2265   time: 3.14s   best: 269.9379\n",
      "Epoch 381/2000:  train Loss: 240.3971   val Loss: 275.0808   time: 3.09s   best: 269.9379\n",
      "Epoch 382/2000:  train Loss: 240.0396   val Loss: 272.3984   time: 3.10s   best: 269.9379\n",
      "Epoch 383/2000:  train Loss: 239.8187   val Loss: 271.5049   time: 3.10s   best: 269.9379\n",
      "Epoch 384/2000:  train Loss: 238.9323   val Loss: 270.3128   time: 3.09s   best: 269.9379\n",
      "Epoch 385/2000:  train Loss: 238.0315   val Loss: 269.4827   time: 3.09s   best: 269.4827\n",
      "Epoch 386/2000:  train Loss: 240.0618   val Loss: 276.1073   time: 3.10s   best: 269.4827\n",
      "Epoch 387/2000:  train Loss: 239.9044   val Loss: 268.5811   time: 3.20s   best: 268.5811\n",
      "Epoch 388/2000:  train Loss: 238.6603   val Loss: 270.7854   time: 3.11s   best: 268.5811\n",
      "Epoch 389/2000:  train Loss: 245.2912   val Loss: 271.6085   time: 3.10s   best: 268.5811\n",
      "Epoch 390/2000:  train Loss: 241.3155   val Loss: 266.9805   time: 3.10s   best: 266.9805\n",
      "Epoch 391/2000:  train Loss: 238.0569   val Loss: 271.7704   time: 3.09s   best: 266.9805\n",
      "Epoch 392/2000:  train Loss: 238.0473   val Loss: 269.4366   time: 3.09s   best: 266.9805\n",
      "Epoch 393/2000:  train Loss: 237.9938   val Loss: 270.0637   time: 3.08s   best: 266.9805\n",
      "Epoch 394/2000:  train Loss: 237.5574   val Loss: 270.6042   time: 3.13s   best: 266.9805\n",
      "Epoch 395/2000:  train Loss: 238.5631   val Loss: 271.8783   time: 3.18s   best: 266.9805\n",
      "Epoch 396/2000:  train Loss: 237.8681   val Loss: 268.6612   time: 3.11s   best: 266.9805\n",
      "Epoch 397/2000:  train Loss: 240.3181   val Loss: 285.8677   time: 3.10s   best: 266.9805\n",
      "Epoch 398/2000:  train Loss: 238.6915   val Loss: 267.1566   time: 3.10s   best: 266.9805\n",
      "Epoch 399/2000:  train Loss: 237.7029   val Loss: 269.3197   time: 3.09s   best: 266.9805\n",
      "Epoch 400/2000:  train Loss: 236.5606   val Loss: 266.5483   time: 3.09s   best: 266.5483\n",
      "Epoch 401/2000:  train Loss: 234.8510   val Loss: 266.8289   time: 3.09s   best: 266.5483\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 402/2000:  train Loss: 238.5602   val Loss: 271.1968   time: 3.16s   best: 266.5483\n",
      "Epoch 403/2000:  train Loss: 237.8092   val Loss: 271.3280   time: 3.16s   best: 266.5483\n",
      "Epoch 404/2000:  train Loss: 236.5716   val Loss: 266.5886   time: 3.10s   best: 266.5483\n",
      "Epoch 405/2000:  train Loss: 235.1886   val Loss: 267.4333   time: 3.10s   best: 266.5483\n",
      "Epoch 406/2000:  train Loss: 236.1909   val Loss: 278.5315   time: 3.10s   best: 266.5483\n",
      "Epoch 407/2000:  train Loss: 240.2236   val Loss: 298.0433   time: 3.09s   best: 266.5483\n",
      "Epoch 408/2000:  train Loss: 240.2614   val Loss: 269.7773   time: 3.09s   best: 266.5483\n",
      "Epoch 409/2000:  train Loss: 234.6314   val Loss: 265.5112   time: 3.10s   best: 265.5112\n",
      "Epoch 410/2000:  train Loss: 234.0571   val Loss: 265.0030   time: 3.19s   best: 265.0030\n",
      "Epoch 411/2000:  train Loss: 235.5683   val Loss: 266.0727   time: 3.13s   best: 265.0030\n",
      "Epoch 412/2000:  train Loss: 239.5969   val Loss: 290.9946   time: 3.10s   best: 265.0030\n",
      "Epoch 413/2000:  train Loss: 236.6240   val Loss: 265.7486   time: 3.11s   best: 265.0030\n",
      "Epoch 414/2000:  train Loss: 233.2268   val Loss: 265.0892   time: 3.10s   best: 265.0030\n",
      "Epoch 415/2000:  train Loss: 236.3902   val Loss: 277.6821   time: 3.08s   best: 265.0030\n",
      "Epoch 416/2000:  train Loss: 236.8381   val Loss: 266.2116   time: 3.08s   best: 265.0030\n",
      "Epoch 417/2000:  train Loss: 233.2800   val Loss: 264.9858   time: 3.10s   best: 264.9858\n",
      "Epoch 418/2000:  train Loss: 232.7062   val Loss: 265.5281   time: 3.20s   best: 264.9858\n",
      "Epoch 419/2000:  train Loss: 235.2641   val Loss: 272.0033   time: 3.13s   best: 264.9858\n",
      "Epoch 420/2000:  train Loss: 234.0864   val Loss: 270.9011   time: 3.11s   best: 264.9858\n",
      "Epoch 421/2000:  train Loss: 238.3226   val Loss: 265.1137   time: 3.10s   best: 264.9858\n",
      "Epoch 422/2000:  train Loss: 233.2263   val Loss: 268.1024   time: 3.09s   best: 264.9858\n",
      "Epoch 423/2000:  train Loss: 233.8423   val Loss: 262.6807   time: 3.09s   best: 262.6807\n",
      "Epoch 424/2000:  train Loss: 233.9702   val Loss: 270.2541   time: 3.08s   best: 262.6807\n",
      "Epoch 425/2000:  train Loss: 233.8689   val Loss: 265.1134   time: 3.11s   best: 262.6807\n",
      "Epoch 426/2000:  train Loss: 232.4683   val Loss: 270.7188   time: 3.19s   best: 262.6807\n",
      "Epoch 427/2000:  train Loss: 234.4439   val Loss: 261.7749   time: 3.11s   best: 261.7749\n",
      "Epoch 428/2000:  train Loss: 231.2217   val Loss: 263.3083   time: 3.10s   best: 261.7749\n",
      "Epoch 429/2000:  train Loss: 233.9993   val Loss: 273.2435   time: 3.10s   best: 261.7749\n",
      "Epoch 430/2000:  train Loss: 232.4145   val Loss: 261.9159   time: 3.09s   best: 261.7749\n",
      "Epoch 431/2000:  train Loss: 231.2788   val Loss: 262.3143   time: 3.08s   best: 261.7749\n",
      "Epoch 432/2000:  train Loss: 234.7494   val Loss: 265.6450   time: 3.08s   best: 261.7749\n",
      "Epoch 433/2000:  train Loss: 236.6509   val Loss: 268.2602   time: 3.15s   best: 261.7749\n",
      "Epoch 434/2000:  train Loss: 232.3283   val Loss: 272.2269   time: 3.16s   best: 261.7749\n",
      "Epoch 435/2000:  train Loss: 233.3287   val Loss: 264.6357   time: 3.10s   best: 261.7749\n",
      "Epoch 436/2000:  train Loss: 232.0389   val Loss: 264.3069   time: 3.09s   best: 261.7749\n",
      "Epoch 437/2000:  train Loss: 231.3370   val Loss: 264.4360   time: 3.09s   best: 261.7749\n",
      "Epoch 438/2000:  train Loss: 230.6319   val Loss: 260.3622   time: 3.09s   best: 260.3622\n",
      "Epoch 439/2000:  train Loss: 231.8302   val Loss: 262.7871   time: 3.08s   best: 260.3622\n",
      "Epoch 440/2000:  train Loss: 231.6603   val Loss: 269.3820   time: 3.09s   best: 260.3622\n",
      "Epoch 441/2000:  train Loss: 237.9412   val Loss: 263.7539   time: 3.18s   best: 260.3622\n",
      "Epoch 442/2000:  train Loss: 233.2755   val Loss: 268.2246   time: 3.14s   best: 260.3622\n",
      "Epoch 443/2000:  train Loss: 231.1484   val Loss: 261.5429   time: 3.10s   best: 260.3622\n",
      "Epoch 444/2000:  train Loss: 231.3051   val Loss: 259.5737   time: 3.09s   best: 259.5737\n",
      "Epoch 445/2000:  train Loss: 231.0923   val Loss: 267.6616   time: 3.10s   best: 259.5737\n",
      "Epoch 446/2000:  train Loss: 231.7009   val Loss: 263.9735   time: 3.09s   best: 259.5737\n",
      "Epoch 447/2000:  train Loss: 229.0515   val Loss: 261.5262   time: 3.08s   best: 259.5737\n",
      "Epoch 448/2000:  train Loss: 231.2097   val Loss: 263.7407   time: 3.10s   best: 259.5737\n",
      "Epoch 449/2000:  train Loss: 230.6185   val Loss: 265.4360   time: 3.18s   best: 259.5737\n",
      "Epoch 450/2000:  train Loss: 231.1285   val Loss: 262.9211   time: 3.12s   best: 259.5737\n",
      "Epoch 451/2000:  train Loss: 230.2596   val Loss: 265.4680   time: 3.09s   best: 259.5737\n",
      "Epoch 452/2000:  train Loss: 236.5349   val Loss: 280.5016   time: 3.09s   best: 259.5737\n",
      "Epoch 453/2000:  train Loss: 232.4489   val Loss: 262.3870   time: 3.09s   best: 259.5737\n",
      "Epoch 454/2000:  train Loss: 228.7125   val Loss: 262.2465   time: 3.08s   best: 259.5737\n",
      "Epoch 455/2000:  train Loss: 228.3802   val Loss: 261.0600   time: 3.08s   best: 259.5737\n",
      "Epoch 456/2000:  train Loss: 229.9618   val Loss: 260.3346   time: 3.12s   best: 259.5737\n",
      "Epoch 457/2000:  train Loss: 230.9871   val Loss: 261.4963   time: 3.18s   best: 259.5737\n",
      "Epoch 458/2000:  train Loss: 230.9894   val Loss: 281.3669   time: 3.10s   best: 259.5737\n",
      "Epoch 459/2000:  train Loss: 231.2491   val Loss: 258.7974   time: 3.09s   best: 258.7974\n",
      "Epoch 460/2000:  train Loss: 232.1038   val Loss: 259.0075   time: 3.10s   best: 258.7974\n",
      "Epoch 461/2000:  train Loss: 229.3757   val Loss: 259.2594   time: 3.09s   best: 258.7974\n",
      "Epoch 462/2000:  train Loss: 228.9480   val Loss: 260.5867   time: 3.09s   best: 258.7974\n",
      "Epoch 463/2000:  train Loss: 227.3238   val Loss: 259.8759   time: 3.08s   best: 258.7974\n",
      "Epoch 464/2000:  train Loss: 229.1184   val Loss: 275.9088   time: 3.14s   best: 258.7974\n",
      "Epoch 465/2000:  train Loss: 230.7470   val Loss: 258.9176   time: 3.17s   best: 258.7974\n",
      "Epoch 466/2000:  train Loss: 228.0984   val Loss: 256.5402   time: 3.10s   best: 256.5402\n",
      "Epoch 467/2000:  train Loss: 228.8873   val Loss: 258.7704   time: 3.10s   best: 256.5402\n",
      "Epoch 468/2000:  train Loss: 227.4464   val Loss: 260.3661   time: 3.10s   best: 256.5402\n",
      "Epoch 469/2000:  train Loss: 227.1436   val Loss: 260.0974   time: 3.08s   best: 256.5402\n",
      "Epoch 470/2000:  train Loss: 228.3391   val Loss: 259.7264   time: 3.08s   best: 256.5402\n",
      "Epoch 471/2000:  train Loss: 228.3538   val Loss: 264.8285   time: 3.09s   best: 256.5402\n",
      "Epoch 472/2000:  train Loss: 227.2152   val Loss: 259.7723   time: 3.18s   best: 256.5402\n",
      "Epoch 473/2000:  train Loss: 227.3433   val Loss: 259.5637   time: 3.13s   best: 256.5402\n",
      "Epoch 474/2000:  train Loss: 226.6969   val Loss: 258.2517   time: 3.09s   best: 256.5402\n",
      "Epoch 475/2000:  train Loss: 226.6731   val Loss: 264.3066   time: 3.10s   best: 256.5402\n",
      "Epoch 476/2000:  train Loss: 237.9394   val Loss: 256.1343   time: 3.11s   best: 256.1343\n",
      "Epoch 477/2000:  train Loss: 227.1268   val Loss: 255.0326   time: 3.09s   best: 255.0326\n",
      "Epoch 478/2000:  train Loss: 226.2766   val Loss: 261.8916   time: 3.09s   best: 255.0326\n",
      "Epoch 479/2000:  train Loss: 227.2517   val Loss: 258.1281   time: 3.12s   best: 255.0326\n",
      "Epoch 480/2000:  train Loss: 225.2323   val Loss: 256.0612   time: 3.18s   best: 255.0326\n",
      "Epoch 481/2000:  train Loss: 224.9890   val Loss: 255.7864   time: 3.12s   best: 255.0326\n",
      "Epoch 482/2000:  train Loss: 225.9507   val Loss: 262.4605   time: 3.10s   best: 255.0326\n",
      "Epoch 483/2000:  train Loss: 226.8935   val Loss: 258.3835   time: 3.10s   best: 255.0326\n",
      "Epoch 484/2000:  train Loss: 224.9320   val Loss: 258.8889   time: 3.09s   best: 255.0326\n",
      "Epoch 485/2000:  train Loss: 226.5782   val Loss: 257.2619   time: 3.09s   best: 255.0326\n",
      "Epoch 486/2000:  train Loss: 227.3217   val Loss: 257.8066   time: 3.08s   best: 255.0326\n",
      "Epoch 487/2000:  train Loss: 225.1468   val Loss: 255.9507   time: 3.15s   best: 255.0326\n",
      "Epoch 488/2000:  train Loss: 225.9285   val Loss: 259.7681   time: 3.17s   best: 255.0326\n",
      "Epoch 489/2000:  train Loss: 225.5122   val Loss: 253.9567   time: 3.10s   best: 253.9567\n",
      "Epoch 490/2000:  train Loss: 226.3989   val Loss: 256.5803   time: 3.10s   best: 253.9567\n",
      "Epoch 491/2000:  train Loss: 226.6744   val Loss: 256.2097   time: 3.10s   best: 253.9567\n",
      "Epoch 492/2000:  train Loss: 226.9122   val Loss: 267.6141   time: 3.09s   best: 253.9567\n",
      "Epoch 493/2000:  train Loss: 227.1614   val Loss: 255.7552   time: 3.09s   best: 253.9567\n",
      "Epoch 494/2000:  train Loss: 225.8294   val Loss: 256.6942   time: 3.09s   best: 253.9567\n",
      "Epoch 495/2000:  train Loss: 225.0297   val Loss: 257.4602   time: 3.18s   best: 253.9567\n",
      "Epoch 496/2000:  train Loss: 224.2326   val Loss: 259.9612   time: 3.15s   best: 253.9567\n",
      "Epoch 497/2000:  train Loss: 224.1704   val Loss: 258.4146   time: 3.10s   best: 253.9567\n",
      "Epoch 498/2000:  train Loss: 225.4588   val Loss: 256.2466   time: 3.10s   best: 253.9567\n",
      "Epoch 499/2000:  train Loss: 224.4707   val Loss: 256.4052   time: 3.10s   best: 253.9567\n",
      "Epoch 500/2000:  train Loss: 225.0848   val Loss: 254.2111   time: 3.09s   best: 253.9567\n",
      "Epoch 501/2000:  train Loss: 223.9725   val Loss: 255.8608   time: 3.09s   best: 253.9567\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 502/2000:  train Loss: 224.8344   val Loss: 259.4284   time: 3.11s   best: 253.9567\n",
      "Epoch 503/2000:  train Loss: 224.7444   val Loss: 256.9749   time: 3.20s   best: 253.9567\n",
      "Epoch 504/2000:  train Loss: 223.2918   val Loss: 260.8860   time: 3.12s   best: 253.9567\n",
      "Epoch 505/2000:  train Loss: 224.5438   val Loss: 259.9967   time: 3.10s   best: 253.9567\n",
      "Epoch 506/2000:  train Loss: 223.4567   val Loss: 255.7958   time: 3.10s   best: 253.9567\n",
      "Epoch 507/2000:  train Loss: 223.3801   val Loss: 254.3756   time: 3.09s   best: 253.9567\n",
      "Epoch 508/2000:  train Loss: 223.6414   val Loss: 255.1764   time: 3.09s   best: 253.9567\n",
      "Epoch 509/2000:  train Loss: 225.9394   val Loss: 269.2715   time: 3.09s   best: 253.9567\n",
      "Epoch 510/2000:  train Loss: 225.5647   val Loss: 254.2455   time: 3.15s   best: 253.9567\n",
      "Epoch 511/2000:  train Loss: 221.7409   val Loss: 256.5946   time: 3.17s   best: 253.9567\n",
      "Epoch 512/2000:  train Loss: 222.1906   val Loss: 255.1378   time: 3.11s   best: 253.9567\n",
      "Epoch 513/2000:  train Loss: 224.7050   val Loss: 262.4622   time: 3.10s   best: 253.9567\n",
      "Epoch 514/2000:  train Loss: 222.8595   val Loss: 257.5615   time: 3.10s   best: 253.9567\n",
      "Epoch 515/2000:  train Loss: 223.7840   val Loss: 251.0606   time: 3.10s   best: 251.0606\n",
      "Epoch 516/2000:  train Loss: 221.6685   val Loss: 251.7644   time: 3.09s   best: 251.0606\n",
      "Epoch 517/2000:  train Loss: 222.2055   val Loss: 259.8913   time: 3.09s   best: 251.0606\n",
      "Epoch 518/2000:  train Loss: 224.0343   val Loss: 256.0898   time: 3.17s   best: 251.0606\n",
      "Epoch 519/2000:  train Loss: 221.9195   val Loss: 252.2130   time: 3.16s   best: 251.0606\n",
      "Epoch 520/2000:  train Loss: 223.0250   val Loss: 253.4168   time: 3.10s   best: 251.0606\n",
      "Epoch 521/2000:  train Loss: 221.9266   val Loss: 254.4088   time: 3.10s   best: 251.0606\n",
      "Epoch 522/2000:  train Loss: 222.3265   val Loss: 265.8745   time: 3.10s   best: 251.0606\n",
      "Epoch 523/2000:  train Loss: 222.7327   val Loss: 252.4842   time: 3.08s   best: 251.0606\n",
      "Epoch 524/2000:  train Loss: 221.9593   val Loss: 254.3014   time: 3.09s   best: 251.0606\n",
      "Epoch 525/2000:  train Loss: 223.1144   val Loss: 255.8306   time: 3.09s   best: 251.0606\n",
      "Epoch 526/2000:  train Loss: 220.3064   val Loss: 251.7512   time: 3.17s   best: 251.0606\n",
      "Epoch 527/2000:  train Loss: 221.9198   val Loss: 253.0458   time: 3.13s   best: 251.0606\n",
      "Epoch 528/2000:  train Loss: 222.5993   val Loss: 261.3689   time: 3.08s   best: 251.0606\n",
      "Epoch 529/2000:  train Loss: 221.3986   val Loss: 253.7645   time: 3.09s   best: 251.0606\n",
      "Epoch 530/2000:  train Loss: 221.5500   val Loss: 255.4921   time: 3.10s   best: 251.0606\n",
      "Epoch 531/2000:  train Loss: 220.7659   val Loss: 253.4266   time: 3.09s   best: 251.0606\n",
      "Epoch 532/2000:  train Loss: 222.1905   val Loss: 255.1431   time: 3.09s   best: 251.0606\n",
      "Epoch 533/2000:  train Loss: 224.2513   val Loss: 253.5099   time: 3.12s   best: 251.0606\n",
      "Epoch 534/2000:  train Loss: 220.8190   val Loss: 251.5584   time: 3.19s   best: 251.0606\n",
      "Epoch 535/2000:  train Loss: 221.0407   val Loss: 252.6329   time: 3.11s   best: 251.0606\n",
      "Epoch 536/2000:  train Loss: 220.5786   val Loss: 257.6040   time: 3.10s   best: 251.0606\n",
      "Epoch 537/2000:  train Loss: 221.3250   val Loss: 252.0870   time: 3.10s   best: 251.0606\n",
      "Epoch 538/2000:  train Loss: 220.4095   val Loss: 251.7942   time: 3.09s   best: 251.0606\n",
      "Epoch 539/2000:  train Loss: 223.2112   val Loss: 251.6992   time: 3.09s   best: 251.0606\n",
      "Epoch 540/2000:  train Loss: 219.5225   val Loss: 253.3249   time: 3.09s   best: 251.0606\n",
      "Epoch 541/2000:  train Loss: 219.4994   val Loss: 251.1575   time: 3.16s   best: 251.0606\n",
      "Epoch 542/2000:  train Loss: 221.3204   val Loss: 253.4523   time: 3.17s   best: 251.0606\n",
      "Epoch 543/2000:  train Loss: 219.4500   val Loss: 252.1205   time: 3.11s   best: 251.0606\n",
      "Epoch 544/2000:  train Loss: 218.1936   val Loss: 252.1481   time: 3.11s   best: 251.0606\n",
      "Epoch 545/2000:  train Loss: 221.1731   val Loss: 264.0681   time: 3.11s   best: 251.0606\n",
      "Epoch 546/2000:  train Loss: 223.0207   val Loss: 250.4868   time: 3.10s   best: 250.4868\n",
      "Epoch 547/2000:  train Loss: 217.7282   val Loss: 252.5277   time: 3.09s   best: 250.4868\n",
      "Epoch 548/2000:  train Loss: 218.7544   val Loss: 253.2416   time: 3.10s   best: 250.4868\n",
      "Epoch 549/2000:  train Loss: 219.1995   val Loss: 252.9146   time: 3.20s   best: 250.4868\n",
      "Epoch 550/2000:  train Loss: 220.0680   val Loss: 251.7727   time: 3.12s   best: 250.4868\n",
      "Epoch 551/2000:  train Loss: 218.5314   val Loss: 252.8646   time: 3.10s   best: 250.4868\n",
      "Epoch 552/2000:  train Loss: 218.3075   val Loss: 255.2396   time: 3.10s   best: 250.4868\n",
      "Epoch 553/2000:  train Loss: 220.8985   val Loss: 251.7588   time: 3.09s   best: 250.4868\n",
      "Epoch 554/2000:  train Loss: 220.6645   val Loss: 250.3425   time: 3.09s   best: 250.3425\n",
      "Epoch 555/2000:  train Loss: 219.8470   val Loss: 251.2688   time: 3.08s   best: 250.3425\n",
      "Epoch 556/2000:  train Loss: 219.6509   val Loss: 250.0196   time: 3.13s   best: 250.0196\n",
      "Epoch 557/2000:  train Loss: 219.8026   val Loss: 248.3328   time: 3.18s   best: 248.3328\n",
      "Epoch 558/2000:  train Loss: 217.1263   val Loss: 248.7831   time: 3.11s   best: 248.3328\n",
      "Epoch 559/2000:  train Loss: 216.4545   val Loss: 250.5458   time: 3.10s   best: 248.3328\n",
      "Epoch 560/2000:  train Loss: 215.9414   val Loss: 250.5894   time: 3.10s   best: 248.3328\n",
      "Epoch 561/2000:  train Loss: 219.4630   val Loss: 253.6847   time: 3.09s   best: 248.3328\n",
      "Epoch 562/2000:  train Loss: 218.3384   val Loss: 250.6828   time: 3.09s   best: 248.3328\n",
      "Epoch 563/2000:  train Loss: 219.7739   val Loss: 252.7468   time: 3.09s   best: 248.3328\n",
      "Epoch 564/2000:  train Loss: 217.6113   val Loss: 248.3860   time: 3.18s   best: 248.3328\n",
      "Epoch 565/2000:  train Loss: 218.5169   val Loss: 253.6087   time: 3.15s   best: 248.3328\n",
      "Epoch 566/2000:  train Loss: 219.4988   val Loss: 248.0546   time: 3.10s   best: 248.0546\n",
      "Epoch 567/2000:  train Loss: 218.9595   val Loss: 250.4216   time: 3.10s   best: 248.0546\n",
      "Epoch 568/2000:  train Loss: 217.3577   val Loss: 250.8535   time: 3.10s   best: 248.0546\n",
      "Epoch 569/2000:  train Loss: 217.1421   val Loss: 250.1909   time: 3.09s   best: 248.0546\n",
      "Epoch 570/2000:  train Loss: 218.2195   val Loss: 250.5400   time: 3.09s   best: 248.0546\n",
      "Epoch 571/2000:  train Loss: 216.3658   val Loss: 250.0241   time: 3.11s   best: 248.0546\n",
      "Epoch 572/2000:  train Loss: 216.6405   val Loss: 248.1370   time: 3.19s   best: 248.0546\n",
      "Epoch 573/2000:  train Loss: 218.3813   val Loss: 253.8177   time: 3.13s   best: 248.0546\n",
      "Epoch 574/2000:  train Loss: 217.2805   val Loss: 248.6375   time: 3.10s   best: 248.0546\n",
      "Epoch 575/2000:  train Loss: 215.9413   val Loss: 247.2395   time: 3.10s   best: 247.2395\n",
      "Epoch 576/2000:  train Loss: 216.1080   val Loss: 249.4055   time: 3.10s   best: 247.2395\n",
      "Epoch 577/2000:  train Loss: 216.0514   val Loss: 251.0214   time: 3.09s   best: 247.2395\n",
      "Epoch 578/2000:  train Loss: 220.7011   val Loss: 248.9648   time: 3.07s   best: 247.2395\n",
      "Epoch 579/2000:  train Loss: 215.2900   val Loss: 250.1558   time: 3.12s   best: 247.2395\n",
      "Epoch 580/2000:  train Loss: 215.5812   val Loss: 247.8859   time: 3.18s   best: 247.2395\n",
      "Epoch 581/2000:  train Loss: 215.3810   val Loss: 252.2988   time: 3.11s   best: 247.2395\n",
      "Epoch 582/2000:  train Loss: 216.2146   val Loss: 249.7061   time: 3.09s   best: 247.2395\n",
      "Epoch 583/2000:  train Loss: 216.9020   val Loss: 252.4596   time: 3.10s   best: 247.2395\n",
      "Epoch 584/2000:  train Loss: 215.6572   val Loss: 248.0768   time: 3.08s   best: 247.2395\n",
      "Epoch 585/2000:  train Loss: 217.0383   val Loss: 255.5133   time: 3.09s   best: 247.2395\n",
      "Epoch 586/2000:  train Loss: 215.8845   val Loss: 248.3190   time: 3.08s   best: 247.2395\n",
      "Epoch 587/2000:  train Loss: 216.0412   val Loss: 246.6259   time: 3.16s   best: 246.6259\n",
      "Epoch 588/2000:  train Loss: 217.4721   val Loss: 248.3318   time: 3.16s   best: 246.6259\n",
      "Epoch 589/2000:  train Loss: 215.2984   val Loss: 248.2293   time: 3.10s   best: 246.6259\n",
      "Epoch 590/2000:  train Loss: 216.5639   val Loss: 251.5421   time: 3.10s   best: 246.6259\n",
      "Epoch 591/2000:  train Loss: 215.0108   val Loss: 247.2418   time: 3.10s   best: 246.6259\n",
      "Epoch 592/2000:  train Loss: 215.8452   val Loss: 251.7353   time: 3.09s   best: 246.6259\n",
      "Epoch 593/2000:  train Loss: 215.5820   val Loss: 247.5775   time: 3.09s   best: 246.6259\n",
      "Epoch 594/2000:  train Loss: 217.9049   val Loss: 249.6734   time: 3.10s   best: 246.6259\n",
      "Epoch 595/2000:  train Loss: 216.0389   val Loss: 249.5087   time: 3.18s   best: 246.6259\n",
      "Epoch 596/2000:  train Loss: 214.3259   val Loss: 246.4761   time: 3.14s   best: 246.4761\n",
      "Epoch 597/2000:  train Loss: 213.4680   val Loss: 248.3118   time: 3.10s   best: 246.4761\n",
      "Epoch 598/2000:  train Loss: 214.0046   val Loss: 254.6634   time: 3.10s   best: 246.4761\n",
      "Epoch 599/2000:  train Loss: 214.7935   val Loss: 247.6506   time: 3.09s   best: 246.4761\n",
      "Epoch 600/2000:  train Loss: 213.4860   val Loss: 253.9704   time: 3.09s   best: 246.4761\n",
      "Epoch 601/2000:  train Loss: 214.6862   val Loss: 248.2316   time: 3.08s   best: 246.4761\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 602/2000:  train Loss: 215.3350   val Loss: 247.7343   time: 3.11s   best: 246.4761\n",
      "Epoch 603/2000:  train Loss: 214.6416   val Loss: 245.9130   time: 3.20s   best: 245.9130\n",
      "Epoch 604/2000:  train Loss: 215.3451   val Loss: 249.2397   time: 3.11s   best: 245.9130\n",
      "Epoch 605/2000:  train Loss: 215.2132   val Loss: 251.7786   time: 3.10s   best: 245.9130\n",
      "Epoch 606/2000:  train Loss: 213.3523   val Loss: 246.0812   time: 3.10s   best: 245.9130\n",
      "Epoch 607/2000:  train Loss: 214.6208   val Loss: 249.9902   time: 3.09s   best: 245.9130\n",
      "Epoch 608/2000:  train Loss: 214.1125   val Loss: 250.1298   time: 3.09s   best: 245.9130\n",
      "Epoch 609/2000:  train Loss: 212.8188   val Loss: 248.1635   time: 3.09s   best: 245.9130\n",
      "Epoch 610/2000:  train Loss: 213.0759   val Loss: 247.5699   time: 3.14s   best: 245.9130\n",
      "Epoch 611/2000:  train Loss: 213.1774   val Loss: 248.5252   time: 3.18s   best: 245.9130\n",
      "Epoch 612/2000:  train Loss: 213.9698   val Loss: 251.6421   time: 3.10s   best: 245.9130\n",
      "Epoch 613/2000:  train Loss: 214.6964   val Loss: 251.0620   time: 3.10s   best: 245.9130\n",
      "Epoch 614/2000:  train Loss: 214.4793   val Loss: 250.2216   time: 3.10s   best: 245.9130\n",
      "Epoch 615/2000:  train Loss: 213.7801   val Loss: 245.3425   time: 3.09s   best: 245.3425\n",
      "Epoch 616/2000:  train Loss: 212.1511   val Loss: 248.3403   time: 3.09s   best: 245.3425\n",
      "Epoch 617/2000:  train Loss: 212.3790   val Loss: 247.2974   time: 3.09s   best: 245.3425\n",
      "Epoch 618/2000:  train Loss: 212.9616   val Loss: 247.9156   time: 3.16s   best: 245.3425\n",
      "Epoch 619/2000:  train Loss: 213.1848   val Loss: 244.7730   time: 3.17s   best: 244.7730\n",
      "Epoch 620/2000:  train Loss: 212.4491   val Loss: 245.5236   time: 3.10s   best: 244.7730\n",
      "Epoch 621/2000:  train Loss: 212.5521   val Loss: 244.5474   time: 3.10s   best: 244.5474\n",
      "Epoch 622/2000:  train Loss: 211.7876   val Loss: 243.4626   time: 3.10s   best: 243.4626\n",
      "Epoch 623/2000:  train Loss: 211.9004   val Loss: 249.2976   time: 3.09s   best: 243.4626\n",
      "Epoch 624/2000:  train Loss: 212.8486   val Loss: 243.7131   time: 3.09s   best: 243.4626\n",
      "Epoch 625/2000:  train Loss: 210.9583   val Loss: 245.9268   time: 3.10s   best: 243.4626\n",
      "Epoch 626/2000:  train Loss: 211.3874   val Loss: 243.8847   time: 3.20s   best: 243.4626\n",
      "Epoch 627/2000:  train Loss: 210.8534   val Loss: 246.1650   time: 3.13s   best: 243.4626\n",
      "Epoch 628/2000:  train Loss: 214.2254   val Loss: 243.0919   time: 3.10s   best: 243.0919\n",
      "Epoch 629/2000:  train Loss: 212.1260   val Loss: 250.6783   time: 3.10s   best: 243.0919\n",
      "Epoch 630/2000:  train Loss: 212.4093   val Loss: 248.0234   time: 3.10s   best: 243.0919\n",
      "Epoch 631/2000:  train Loss: 210.6581   val Loss: 244.4416   time: 3.09s   best: 243.0919\n",
      "Epoch 632/2000:  train Loss: 211.2333   val Loss: 249.1904   time: 3.09s   best: 243.0919\n",
      "Epoch 633/2000:  train Loss: 213.5901   val Loss: 246.5259   time: 3.12s   best: 243.0919\n",
      "Epoch 634/2000:  train Loss: 211.4228   val Loss: 245.6126   time: 3.20s   best: 243.0919\n",
      "Epoch 635/2000:  train Loss: 211.0556   val Loss: 246.0120   time: 3.24s   best: 243.0919\n",
      "Epoch 636/2000:  train Loss: 211.6315   val Loss: 242.7045   time: 3.22s   best: 242.7045\n",
      "Epoch 637/2000:  train Loss: 210.3135   val Loss: 242.2020   time: 3.22s   best: 242.2020\n",
      "Epoch 638/2000:  train Loss: 214.5006   val Loss: 247.4504   time: 3.21s   best: 242.2020\n",
      "Epoch 639/2000:  train Loss: 211.5992   val Loss: 249.0752   time: 3.16s   best: 242.2020\n",
      "Epoch 640/2000:  train Loss: 211.8308   val Loss: 242.8335   time: 3.09s   best: 242.2020\n",
      "Epoch 641/2000:  train Loss: 209.7415   val Loss: 243.7018   time: 3.16s   best: 242.2020\n",
      "Epoch 642/2000:  train Loss: 210.2628   val Loss: 241.9636   time: 3.17s   best: 241.9636\n",
      "Epoch 643/2000:  train Loss: 210.4460   val Loss: 242.6054   time: 3.10s   best: 241.9636\n",
      "Epoch 644/2000:  train Loss: 209.5770   val Loss: 244.2618   time: 3.10s   best: 241.9636\n",
      "Epoch 645/2000:  train Loss: 210.9323   val Loss: 242.9233   time: 3.10s   best: 241.9636\n",
      "Epoch 646/2000:  train Loss: 211.3804   val Loss: 241.9020   time: 3.09s   best: 241.9020\n",
      "Epoch 647/2000:  train Loss: 210.9493   val Loss: 243.2419   time: 3.09s   best: 241.9020\n",
      "Epoch 648/2000:  train Loss: 211.0014   val Loss: 240.2817   time: 3.09s   best: 240.2817\n",
      "Epoch 649/2000:  train Loss: 214.3164   val Loss: 242.3319   time: 3.19s   best: 240.2817\n",
      "Epoch 650/2000:  train Loss: 210.8341   val Loss: 240.6255   time: 3.14s   best: 240.2817\n",
      "Epoch 651/2000:  train Loss: 208.6672   val Loss: 241.0036   time: 3.10s   best: 240.2817\n",
      "Epoch 652/2000:  train Loss: 210.0502   val Loss: 243.9960   time: 3.10s   best: 240.2817\n",
      "Epoch 653/2000:  train Loss: 209.3159   val Loss: 243.0211   time: 3.10s   best: 240.2817\n",
      "Epoch 654/2000:  train Loss: 209.7569   val Loss: 245.6964   time: 3.10s   best: 240.2817\n",
      "Epoch 655/2000:  train Loss: 210.5330   val Loss: 240.9731   time: 3.08s   best: 240.2817\n",
      "Epoch 656/2000:  train Loss: 208.8605   val Loss: 246.1543   time: 3.12s   best: 240.2817\n",
      "Epoch 657/2000:  train Loss: 210.7792   val Loss: 243.5930   time: 3.19s   best: 240.2817\n",
      "Epoch 658/2000:  train Loss: 208.6067   val Loss: 243.0551   time: 3.13s   best: 240.2817\n",
      "Epoch 659/2000:  train Loss: 210.8486   val Loss: 242.2210   time: 3.10s   best: 240.2817\n",
      "Epoch 660/2000:  train Loss: 208.8480   val Loss: 241.8219   time: 3.10s   best: 240.2817\n",
      "Epoch 661/2000:  train Loss: 211.6206   val Loss: 240.4817   time: 3.10s   best: 240.2817\n",
      "Epoch 662/2000:  train Loss: 207.8915   val Loss: 240.4156   time: 3.09s   best: 240.2817\n",
      "Epoch 663/2000:  train Loss: 207.9112   val Loss: 240.5722   time: 3.09s   best: 240.2817\n",
      "Epoch 664/2000:  train Loss: 210.8689   val Loss: 246.5774   time: 3.12s   best: 240.2817\n",
      "Epoch 665/2000:  train Loss: 209.6738   val Loss: 243.6101   time: 3.21s   best: 240.2817\n",
      "Epoch 666/2000:  train Loss: 209.6767   val Loss: 241.7585   time: 3.12s   best: 240.2817\n",
      "Epoch 667/2000:  train Loss: 208.3884   val Loss: 241.1701   time: 3.10s   best: 240.2817\n",
      "Epoch 668/2000:  train Loss: 208.5125   val Loss: 240.7889   time: 3.10s   best: 240.2817\n",
      "Epoch 669/2000:  train Loss: 207.7485   val Loss: 239.9964   time: 3.09s   best: 239.9964\n",
      "Epoch 670/2000:  train Loss: 207.7044   val Loss: 242.6288   time: 3.09s   best: 239.9964\n",
      "Epoch 671/2000:  train Loss: 209.2437   val Loss: 244.4503   time: 3.09s   best: 239.9964\n",
      "Epoch 672/2000:  train Loss: 208.4834   val Loss: 242.5180   time: 3.16s   best: 239.9964\n",
      "Epoch 673/2000:  train Loss: 208.5999   val Loss: 246.1579   time: 3.13s   best: 239.9964\n",
      "Epoch 674/2000:  train Loss: 208.8393   val Loss: 239.7711   time: 3.10s   best: 239.7711\n",
      "Epoch 675/2000:  train Loss: 207.5040   val Loss: 243.2197   time: 3.08s   best: 239.7711\n",
      "Epoch 676/2000:  train Loss: 207.5070   val Loss: 241.4241   time: 3.10s   best: 239.7711\n",
      "Epoch 677/2000:  train Loss: 208.5598   val Loss: 252.2403   time: 3.09s   best: 239.7711\n",
      "Epoch 678/2000:  train Loss: 209.1983   val Loss: 248.6467   time: 3.09s   best: 239.7711\n",
      "Epoch 679/2000:  train Loss: 209.4727   val Loss: 243.8601   time: 3.09s   best: 239.7711\n",
      "Epoch 680/2000:  train Loss: 208.2297   val Loss: 242.4048   time: 3.18s   best: 239.7711\n",
      "Epoch 681/2000:  train Loss: 209.6204   val Loss: 240.3677   time: 3.16s   best: 239.7711\n",
      "Epoch 682/2000:  train Loss: 207.7320   val Loss: 239.0470   time: 3.10s   best: 239.0470\n",
      "Epoch 683/2000:  train Loss: 206.5285   val Loss: 239.4294   time: 3.10s   best: 239.0470\n",
      "Epoch 684/2000:  train Loss: 206.8588   val Loss: 240.3036   time: 3.10s   best: 239.0470\n",
      "Epoch 685/2000:  train Loss: 209.8074   val Loss: 246.2323   time: 3.10s   best: 239.0470\n",
      "Epoch 686/2000:  train Loss: 207.3682   val Loss: 238.5082   time: 3.09s   best: 238.5082\n",
      "Epoch 687/2000:  train Loss: 206.3711   val Loss: 243.3589   time: 3.10s   best: 238.5082\n",
      "Epoch 688/2000:  train Loss: 206.3344   val Loss: 242.4923   time: 3.20s   best: 238.5082\n",
      "Epoch 689/2000:  train Loss: 207.5524   val Loss: 242.9417   time: 3.11s   best: 238.5082\n",
      "Epoch 690/2000:  train Loss: 212.3733   val Loss: 238.0469   time: 3.09s   best: 238.0469\n",
      "Epoch 691/2000:  train Loss: 208.0508   val Loss: 237.6857   time: 3.09s   best: 237.6857\n",
      "Epoch 692/2000:  train Loss: 205.9089   val Loss: 239.1649   time: 3.09s   best: 237.6857\n",
      "Epoch 693/2000:  train Loss: 205.9938   val Loss: 238.8424   time: 3.08s   best: 237.6857\n",
      "Epoch 694/2000:  train Loss: 206.1419   val Loss: 238.4793   time: 3.10s   best: 237.6857\n",
      "Epoch 695/2000:  train Loss: 205.7272   val Loss: 238.1047   time: 3.14s   best: 237.6857\n",
      "Epoch 696/2000:  train Loss: 206.1537   val Loss: 241.6592   time: 3.20s   best: 237.6857\n",
      "Epoch 697/2000:  train Loss: 206.6880   val Loss: 248.1157   time: 3.12s   best: 237.6857\n",
      "Epoch 698/2000:  train Loss: 206.6560   val Loss: 240.2536   time: 3.11s   best: 237.6857\n",
      "Epoch 699/2000:  train Loss: 209.3679   val Loss: 238.5596   time: 3.11s   best: 237.6857\n",
      "Epoch 700/2000:  train Loss: 207.8905   val Loss: 239.3731   time: 3.09s   best: 237.6857\n",
      "Epoch 701/2000:  train Loss: 205.6988   val Loss: 240.6838   time: 3.29s   best: 237.6857\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 702/2000:  train Loss: 206.4877   val Loss: 239.8684   time: 3.50s   best: 237.6857\n",
      "Epoch 703/2000:  train Loss: 205.3320   val Loss: 240.2873   time: 3.27s   best: 237.6857\n",
      "Epoch 704/2000:  train Loss: 205.7826   val Loss: 240.8168   time: 3.14s   best: 237.6857\n",
      "Epoch 705/2000:  train Loss: 205.1162   val Loss: 241.1606   time: 3.09s   best: 237.6857\n",
      "Epoch 706/2000:  train Loss: 205.5649   val Loss: 243.6686   time: 3.09s   best: 237.6857\n",
      "Epoch 707/2000:  train Loss: 205.3109   val Loss: 241.5465   time: 3.09s   best: 237.6857\n",
      "Epoch 708/2000:  train Loss: 204.9189   val Loss: 243.1508   time: 3.08s   best: 237.6857\n",
      "Epoch 709/2000:  train Loss: 206.2033   val Loss: 244.0567   time: 3.08s   best: 237.6857\n",
      "Epoch 710/2000:  train Loss: 207.1408   val Loss: 238.4221   time: 3.10s   best: 237.6857\n",
      "Epoch 711/2000:  train Loss: 204.7552   val Loss: 240.0174   time: 3.18s   best: 237.6857\n",
      "Epoch 712/2000:  train Loss: 205.8629   val Loss: 240.5613   time: 3.10s   best: 237.6857\n",
      "Epoch 713/2000:  train Loss: 206.4530   val Loss: 238.9525   time: 3.09s   best: 237.6857\n",
      "Epoch 714/2000:  train Loss: 204.4773   val Loss: 240.3505   time: 3.09s   best: 237.6857\n",
      "Epoch 715/2000:  train Loss: 205.0144   val Loss: 244.0142   time: 3.08s   best: 237.6857\n",
      "Epoch 716/2000:  train Loss: 204.8191   val Loss: 238.7541   time: 3.08s   best: 237.6857\n",
      "Epoch 717/2000:  train Loss: 205.2539   val Loss: 243.0864   time: 3.08s   best: 237.6857\n",
      "Epoch 718/2000:  train Loss: 206.4473   val Loss: 238.5666   time: 3.13s   best: 237.6857\n",
      "Epoch 719/2000:  train Loss: 203.5735   val Loss: 238.2002   time: 3.17s   best: 237.6857\n",
      "Epoch 720/2000:  train Loss: 203.9653   val Loss: 238.3809   time: 3.09s   best: 237.6857\n",
      "Epoch 721/2000:  train Loss: 206.4215   val Loss: 246.0736   time: 3.09s   best: 237.6857\n",
      "Epoch 722/2000:  train Loss: 207.9228   val Loss: 240.8360   time: 3.09s   best: 237.6857\n",
      "Epoch 723/2000:  train Loss: 204.9835   val Loss: 241.3526   time: 3.08s   best: 237.6857\n",
      "Epoch 724/2000:  train Loss: 204.1705   val Loss: 238.4473   time: 3.07s   best: 237.6857\n",
      "Epoch 725/2000:  train Loss: 204.1026   val Loss: 237.9987   time: 3.08s   best: 237.6857\n",
      "Epoch 726/2000:  train Loss: 203.0908   val Loss: 243.4807   time: 3.15s   best: 237.6857\n",
      "Epoch 727/2000:  train Loss: 205.1006   val Loss: 240.7732   time: 3.15s   best: 237.6857\n",
      "Epoch 728/2000:  train Loss: 203.8658   val Loss: 239.0813   time: 3.09s   best: 237.6857\n",
      "Epoch 729/2000:  train Loss: 205.1553   val Loss: 240.8826   time: 3.09s   best: 237.6857\n",
      "Epoch 730/2000:  train Loss: 208.6441   val Loss: 235.8010   time: 3.09s   best: 235.8010\n",
      "Epoch 731/2000:  train Loss: 203.4883   val Loss: 238.0913   time: 3.08s   best: 235.8010\n",
      "Epoch 732/2000:  train Loss: 204.8578   val Loss: 238.7603   time: 3.08s   best: 235.8010\n",
      "Epoch 733/2000:  train Loss: 202.6190   val Loss: 237.3252   time: 3.09s   best: 235.8010\n",
      "Epoch 734/2000:  train Loss: 202.1323   val Loss: 237.8481   time: 3.18s   best: 235.8010\n",
      "Epoch 735/2000:  train Loss: 203.4074   val Loss: 237.6995   time: 3.12s   best: 235.8010\n",
      "Epoch 736/2000:  train Loss: 203.4230   val Loss: 241.9953   time: 3.09s   best: 235.8010\n",
      "Epoch 737/2000:  train Loss: 203.9440   val Loss: 241.4213   time: 3.09s   best: 235.8010\n",
      "Epoch 738/2000:  train Loss: 203.0612   val Loss: 241.2455   time: 3.09s   best: 235.8010\n",
      "Epoch 739/2000:  train Loss: 204.2650   val Loss: 237.3183   time: 3.08s   best: 235.8010\n",
      "Epoch 740/2000:  train Loss: 205.4387   val Loss: 242.6723   time: 3.08s   best: 235.8010\n",
      "Epoch 741/2000:  train Loss: 204.0887   val Loss: 237.6643   time: 3.10s   best: 235.8010\n",
      "Epoch 742/2000:  train Loss: 202.9348   val Loss: 238.0126   time: 3.19s   best: 235.8010\n",
      "Epoch 743/2000:  train Loss: 205.3425   val Loss: 244.9615   time: 3.09s   best: 235.8010\n",
      "Epoch 744/2000:  train Loss: 205.9309   val Loss: 235.3590   time: 3.09s   best: 235.3590\n",
      "Epoch 745/2000:  train Loss: 203.1297   val Loss: 239.8500   time: 3.17s   best: 235.3590\n",
      "Epoch 746/2000:  train Loss: 205.3178   val Loss: 237.3198   time: 3.22s   best: 235.3590\n",
      "Epoch 747/2000:  train Loss: 203.5698   val Loss: 236.5589   time: 3.21s   best: 235.3590\n",
      "Epoch 748/2000:  train Loss: 201.7785   val Loss: 237.0930   time: 3.19s   best: 235.3590\n",
      "Epoch 749/2000:  train Loss: 202.5191   val Loss: 240.9833   time: 3.19s   best: 235.3590\n",
      "Epoch 750/2000:  train Loss: 203.9346   val Loss: 238.8382   time: 3.20s   best: 235.3590\n",
      "Epoch 751/2000:  train Loss: 203.3451   val Loss: 236.5383   time: 3.11s   best: 235.3590\n",
      "Epoch 752/2000:  train Loss: 202.1373   val Loss: 236.5677   time: 3.11s   best: 235.3590\n",
      "Epoch 753/2000:  train Loss: 203.5278   val Loss: 239.0641   time: 3.11s   best: 235.3590\n",
      "Epoch 754/2000:  train Loss: 202.9676   val Loss: 238.1066   time: 3.10s   best: 235.3590\n",
      "Epoch 755/2000:  train Loss: 204.3331   val Loss: 236.8577   time: 3.10s   best: 235.3590\n",
      "Epoch 756/2000:  train Loss: 203.4221   val Loss: 237.0805   time: 3.10s   best: 235.3590\n",
      "Epoch 757/2000:  train Loss: 203.3056   val Loss: 241.5412   time: 3.17s   best: 235.3590\n",
      "Epoch 758/2000:  train Loss: 203.2455   val Loss: 235.7643   time: 3.18s   best: 235.3590\n",
      "Epoch 759/2000:  train Loss: 202.7571   val Loss: 236.8521   time: 3.11s   best: 235.3590\n",
      "Epoch 760/2000:  train Loss: 201.6413   val Loss: 236.4156   time: 3.11s   best: 235.3590\n",
      "Epoch 761/2000:  train Loss: 201.5865   val Loss: 237.3922   time: 3.11s   best: 235.3590\n",
      "Epoch 762/2000:  train Loss: 201.4603   val Loss: 238.5950   time: 3.10s   best: 235.3590\n",
      "Epoch 763/2000:  train Loss: 202.5362   val Loss: 238.7238   time: 3.10s   best: 235.3590\n",
      "Epoch 764/2000:  train Loss: 201.6118   val Loss: 236.6111   time: 3.11s   best: 235.3590\n",
      "Epoch 765/2000:  train Loss: 201.8556   val Loss: 240.1406   time: 3.20s   best: 235.3590\n",
      "Epoch 766/2000:  train Loss: 202.0027   val Loss: 238.3827   time: 3.14s   best: 235.3590\n",
      "Epoch 767/2000:  train Loss: 203.3571   val Loss: 236.8611   time: 3.11s   best: 235.3590\n",
      "Epoch 768/2000:  train Loss: 203.2757   val Loss: 235.8862   time: 3.10s   best: 235.3590\n",
      "Epoch 769/2000:  train Loss: 201.3850   val Loss: 234.6524   time: 3.10s   best: 234.6524\n",
      "Epoch 770/2000:  train Loss: 200.7078   val Loss: 235.8815   time: 3.09s   best: 234.6524\n",
      "Epoch 771/2000:  train Loss: 200.5381   val Loss: 237.9528   time: 3.08s   best: 234.6524\n",
      "Epoch 772/2000:  train Loss: 200.5697   val Loss: 236.8099   time: 3.12s   best: 234.6524\n",
      "Epoch 773/2000:  train Loss: 202.1166   val Loss: 237.3177   time: 3.18s   best: 234.6524\n",
      "Epoch 774/2000:  train Loss: 201.7243   val Loss: 242.0308   time: 3.11s   best: 234.6524\n",
      "Epoch 775/2000:  train Loss: 204.0371   val Loss: 234.0725   time: 3.09s   best: 234.0725\n",
      "Epoch 776/2000:  train Loss: 202.2284   val Loss: 232.4506   time: 3.10s   best: 232.4506\n",
      "Epoch 777/2000:  train Loss: 201.0057   val Loss: 235.9332   time: 3.09s   best: 232.4506\n",
      "Epoch 778/2000:  train Loss: 200.9025   val Loss: 233.2142   time: 3.08s   best: 232.4506\n",
      "Epoch 779/2000:  train Loss: 200.3463   val Loss: 239.1283   time: 3.08s   best: 232.4506\n",
      "Epoch 780/2000:  train Loss: 201.0693   val Loss: 234.5968   time: 3.15s   best: 232.4506\n",
      "Epoch 781/2000:  train Loss: 199.0372   val Loss: 234.6567   time: 3.17s   best: 232.4506\n",
      "Epoch 782/2000:  train Loss: 200.8760   val Loss: 242.5319   time: 3.10s   best: 232.4506\n",
      "Epoch 783/2000:  train Loss: 201.8059   val Loss: 235.5274   time: 3.10s   best: 232.4506\n",
      "Epoch 784/2000:  train Loss: 201.7661   val Loss: 233.7444   time: 3.09s   best: 232.4506\n",
      "Epoch 785/2000:  train Loss: 199.5516   val Loss: 237.9923   time: 3.09s   best: 232.4506\n",
      "Epoch 786/2000:  train Loss: 203.5605   val Loss: 253.7255   time: 3.09s   best: 232.4506\n",
      "Epoch 787/2000:  train Loss: 206.0410   val Loss: 233.9398   time: 3.09s   best: 232.4506\n",
      "Epoch 788/2000:  train Loss: 200.0559   val Loss: 235.3748   time: 3.18s   best: 232.4506\n",
      "Epoch 789/2000:  train Loss: 199.2767   val Loss: 235.4730   time: 3.14s   best: 232.4506\n",
      "Epoch 790/2000:  train Loss: 200.1519   val Loss: 234.4738   time: 3.09s   best: 232.4506\n",
      "Epoch 791/2000:  train Loss: 200.7923   val Loss: 235.2679   time: 3.09s   best: 232.4506\n",
      "Epoch 792/2000:  train Loss: 201.3894   val Loss: 234.8463   time: 3.09s   best: 232.4506\n",
      "Epoch 793/2000:  train Loss: 200.3462   val Loss: 235.3316   time: 3.09s   best: 232.4506\n",
      "Epoch 794/2000:  train Loss: 198.8230   val Loss: 236.0695   time: 3.08s   best: 232.4506\n",
      "Epoch 795/2000:  train Loss: 199.6102   val Loss: 237.0766   time: 3.11s   best: 232.4506\n",
      "Epoch 796/2000:  train Loss: 199.5053   val Loss: 235.3790   time: 3.19s   best: 232.4506\n",
      "Epoch 797/2000:  train Loss: 199.5823   val Loss: 236.4535   time: 3.12s   best: 232.4506\n",
      "Epoch 798/2000:  train Loss: 200.3225   val Loss: 235.6987   time: 3.09s   best: 232.4506\n",
      "Epoch 799/2000:  train Loss: 199.3158   val Loss: 237.3789   time: 3.10s   best: 232.4506\n",
      "Epoch 800/2000:  train Loss: 200.0910   val Loss: 237.8868   time: 3.08s   best: 232.4506\n",
      "Epoch 801/2000:  train Loss: 200.9040   val Loss: 233.6636   time: 3.08s   best: 232.4506\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 802/2000:  train Loss: 199.3607   val Loss: 237.5363   time: 3.08s   best: 232.4506\n",
      "Epoch 803/2000:  train Loss: 199.9283   val Loss: 237.8902   time: 3.13s   best: 232.4506\n",
      "Epoch 804/2000:  train Loss: 200.7880   val Loss: 232.1295   time: 3.19s   best: 232.1295\n",
      "Epoch 805/2000:  train Loss: 198.9066   val Loss: 233.5173   time: 3.11s   best: 232.1295\n",
      "Epoch 806/2000:  train Loss: 199.0945   val Loss: 236.8535   time: 3.10s   best: 232.1295\n",
      "Epoch 807/2000:  train Loss: 199.7051   val Loss: 234.7507   time: 3.10s   best: 232.1295\n",
      "Epoch 808/2000:  train Loss: 199.7379   val Loss: 236.3577   time: 3.09s   best: 232.1295\n",
      "Epoch 809/2000:  train Loss: 200.0888   val Loss: 234.9952   time: 3.09s   best: 232.1295\n",
      "Epoch 810/2000:  train Loss: 201.0181   val Loss: 238.3973   time: 3.09s   best: 232.1295\n",
      "Epoch 811/2000:  train Loss: 199.4470   val Loss: 237.0900   time: 3.16s   best: 232.1295\n",
      "Epoch 812/2000:  train Loss: 201.5283   val Loss: 233.9197   time: 3.17s   best: 232.1295\n",
      "Epoch 813/2000:  train Loss: 200.3152   val Loss: 233.1605   time: 3.10s   best: 232.1295\n",
      "Epoch 814/2000:  train Loss: 198.5310   val Loss: 233.3031   time: 3.10s   best: 232.1295\n",
      "Epoch 815/2000:  train Loss: 199.6392   val Loss: 233.4753   time: 3.10s   best: 232.1295\n",
      "Epoch 816/2000:  train Loss: 199.1601   val Loss: 237.7776   time: 3.09s   best: 232.1295\n",
      "Epoch 817/2000:  train Loss: 200.1439   val Loss: 232.7388   time: 3.09s   best: 232.1295\n",
      "Epoch 818/2000:  train Loss: 198.7718   val Loss: 233.1923   time: 3.10s   best: 232.1295\n",
      "Epoch 819/2000:  train Loss: 197.8125   val Loss: 235.5429   time: 3.20s   best: 232.1295\n",
      "Epoch 820/2000:  train Loss: 198.2769   val Loss: 234.3089   time: 3.13s   best: 232.1295\n",
      "Epoch 821/2000:  train Loss: 198.3537   val Loss: 234.0766   time: 3.10s   best: 232.1295\n",
      "Epoch 822/2000:  train Loss: 197.2186   val Loss: 235.3108   time: 3.10s   best: 232.1295\n",
      "Epoch 823/2000:  train Loss: 200.9102   val Loss: 238.2459   time: 3.10s   best: 232.1295\n",
      "Epoch 824/2000:  train Loss: 200.7272   val Loss: 231.4098   time: 3.09s   best: 231.4098\n",
      "Epoch 825/2000:  train Loss: 197.4510   val Loss: 235.7114   time: 3.09s   best: 231.4098\n",
      "Epoch 826/2000:  train Loss: 197.7281   val Loss: 232.8308   time: 3.13s   best: 231.4098\n",
      "Epoch 827/2000:  train Loss: 197.9114   val Loss: 236.2182   time: 3.19s   best: 231.4098\n",
      "Epoch 828/2000:  train Loss: 197.2976   val Loss: 232.5838   time: 3.11s   best: 231.4098\n",
      "Epoch 829/2000:  train Loss: 198.1987   val Loss: 241.4764   time: 3.10s   best: 231.4098\n",
      "Epoch 830/2000:  train Loss: 199.0682   val Loss: 236.8085   time: 3.10s   best: 231.4098\n",
      "Epoch 831/2000:  train Loss: 197.6007   val Loss: 233.8162   time: 3.09s   best: 231.4098\n",
      "Epoch 832/2000:  train Loss: 196.9833   val Loss: 237.4156   time: 3.09s   best: 231.4098\n",
      "Epoch 833/2000:  train Loss: 198.2570   val Loss: 235.0141   time: 3.09s   best: 231.4098\n",
      "Epoch 834/2000:  train Loss: 198.6484   val Loss: 236.6136   time: 3.15s   best: 231.4098\n",
      "Epoch 835/2000:  train Loss: 198.5085   val Loss: 232.9075   time: 3.18s   best: 231.4098\n",
      "Epoch 836/2000:  train Loss: 197.1487   val Loss: 233.4743   time: 3.10s   best: 231.4098\n",
      "Epoch 837/2000:  train Loss: 196.9422   val Loss: 232.5766   time: 3.10s   best: 231.4098\n",
      "Epoch 838/2000:  train Loss: 197.4033   val Loss: 238.1853   time: 3.10s   best: 231.4098\n",
      "Epoch 839/2000:  train Loss: 198.8008   val Loss: 234.0203   time: 3.09s   best: 231.4098\n",
      "Epoch 840/2000:  train Loss: 199.1435   val Loss: 232.7265   time: 3.09s   best: 231.4098\n",
      "Epoch 841/2000:  train Loss: 197.5361   val Loss: 233.0166   time: 3.09s   best: 231.4098\n",
      "Epoch 842/2000:  train Loss: 196.1990   val Loss: 234.3915   time: 3.17s   best: 231.4098\n",
      "Epoch 843/2000:  train Loss: 196.7443   val Loss: 234.6953   time: 3.15s   best: 231.4098\n",
      "Epoch 844/2000:  train Loss: 197.4239   val Loss: 233.6771   time: 3.10s   best: 231.4098\n",
      "Epoch 845/2000:  train Loss: 196.4469   val Loss: 235.3402   time: 3.10s   best: 231.4098\n",
      "Epoch 846/2000:  train Loss: 196.0210   val Loss: 234.0473   time: 3.10s   best: 231.4098\n",
      "Epoch 847/2000:  train Loss: 197.4267   val Loss: 238.6966   time: 3.09s   best: 231.4098\n",
      "Epoch 848/2000:  train Loss: 197.3948   val Loss: 233.0683   time: 3.08s   best: 231.4098\n",
      "Epoch 849/2000:  train Loss: 197.0123   val Loss: 235.8752   time: 3.09s   best: 231.4098\n",
      "Epoch 850/2000:  train Loss: 196.5136   val Loss: 233.0575   time: 3.19s   best: 231.4098\n",
      "Epoch 851/2000:  train Loss: 195.9221   val Loss: 238.8106   time: 3.13s   best: 231.4098\n",
      "Epoch 852/2000:  train Loss: 196.5237   val Loss: 234.4999   time: 3.10s   best: 231.4098\n",
      "Epoch 853/2000:  train Loss: 195.6191   val Loss: 233.5968   time: 3.09s   best: 231.4098\n",
      "Epoch 854/2000:  train Loss: 196.4521   val Loss: 236.8502   time: 3.10s   best: 231.4098\n",
      "Epoch 855/2000:  train Loss: 195.7552   val Loss: 233.7688   time: 3.08s   best: 231.4098\n",
      "Epoch 856/2000:  train Loss: 195.9211   val Loss: 238.7033   time: 3.08s   best: 231.4098\n",
      "Epoch 857/2000:  train Loss: 198.1160   val Loss: 235.4826   time: 3.10s   best: 231.4098\n",
      "Epoch 858/2000:  train Loss: 197.6506   val Loss: 232.6833   time: 3.19s   best: 231.4098\n",
      "Epoch 859/2000:  train Loss: 197.8297   val Loss: 230.0701   time: 3.10s   best: 230.0701\n",
      "Epoch 860/2000:  train Loss: 195.1760   val Loss: 231.2431   time: 3.10s   best: 230.0701\n",
      "Epoch 861/2000:  train Loss: 195.8715   val Loss: 232.8063   time: 3.10s   best: 230.0701\n",
      "Epoch 862/2000:  train Loss: 195.6468   val Loss: 232.1278   time: 3.09s   best: 230.0701\n",
      "Epoch 863/2000:  train Loss: 195.8746   val Loss: 243.3644   time: 3.09s   best: 230.0701\n",
      "Epoch 864/2000:  train Loss: 198.7633   val Loss: 233.4277   time: 3.08s   best: 230.0701\n",
      "Epoch 865/2000:  train Loss: 194.3342   val Loss: 233.1491   time: 3.12s   best: 230.0701\n",
      "Epoch 866/2000:  train Loss: 196.1903   val Loss: 234.5255   time: 3.18s   best: 230.0701\n",
      "Epoch 867/2000:  train Loss: 196.8382   val Loss: 234.0022   time: 3.11s   best: 230.0701\n",
      "Epoch 868/2000:  train Loss: 194.4536   val Loss: 237.8542   time: 3.10s   best: 230.0701\n",
      "Epoch 869/2000:  train Loss: 195.1843   val Loss: 233.3272   time: 3.10s   best: 230.0701\n",
      "Epoch 870/2000:  train Loss: 196.2823   val Loss: 232.4238   time: 3.09s   best: 230.0701\n",
      "Epoch 871/2000:  train Loss: 197.0650   val Loss: 244.3225   time: 3.09s   best: 230.0701\n",
      "Epoch 872/2000:  train Loss: 195.9314   val Loss: 234.7830   time: 3.08s   best: 230.0701\n",
      "Epoch 873/2000:  train Loss: 195.2293   val Loss: 233.6477   time: 3.15s   best: 230.0701\n",
      "Epoch 874/2000:  train Loss: 195.0559   val Loss: 235.1625   time: 3.17s   best: 230.0701\n",
      "Epoch 875/2000:  train Loss: 194.8684   val Loss: 234.7269   time: 3.10s   best: 230.0701\n",
      "Epoch 876/2000:  train Loss: 194.4488   val Loss: 232.6868   time: 3.10s   best: 230.0701\n",
      "Epoch 877/2000:  train Loss: 194.1010   val Loss: 237.7992   time: 3.09s   best: 230.0701\n",
      "Epoch 878/2000:  train Loss: 195.8445   val Loss: 233.6217   time: 3.09s   best: 230.0701\n",
      "Epoch 879/2000:  train Loss: 194.5480   val Loss: 233.9734   time: 3.09s   best: 230.0701\n",
      "Epoch 880/2000:  train Loss: 194.3487   val Loss: 233.4300   time: 3.09s   best: 230.0701\n",
      "Epoch 881/2000:  train Loss: 195.0072   val Loss: 234.2635   time: 3.18s   best: 230.0701\n",
      "Epoch 882/2000:  train Loss: 195.0600   val Loss: 233.7832   time: 3.14s   best: 230.0701\n",
      "Epoch 883/2000:  train Loss: 195.6081   val Loss: 242.0790   time: 3.10s   best: 230.0701\n",
      "Epoch 884/2000:  train Loss: 194.6625   val Loss: 232.2242   time: 3.10s   best: 230.0701\n",
      "Epoch 885/2000:  train Loss: 196.8438   val Loss: 233.6744   time: 3.10s   best: 230.0701\n",
      "Epoch 886/2000:  train Loss: 195.6503   val Loss: 231.9466   time: 3.14s   best: 230.0701\n",
      "Epoch 887/2000:  train Loss: 194.7647   val Loss: 238.5358   time: 3.21s   best: 230.0701\n",
      "Epoch 888/2000:  train Loss: 194.9815   val Loss: 234.5986   time: 3.28s   best: 230.0701\n",
      "Epoch 889/2000:  train Loss: 194.0497   val Loss: 234.8614   time: 3.33s   best: 230.0701\n",
      "Epoch 890/2000:  train Loss: 196.2926   val Loss: 231.2719   time: 3.24s   best: 230.0701\n",
      "Epoch 891/2000:  train Loss: 194.2917   val Loss: 233.5142   time: 3.24s   best: 230.0701\n",
      "Epoch 892/2000:  train Loss: 193.9391   val Loss: 231.9056   time: 3.24s   best: 230.0701\n",
      "Epoch 893/2000:  train Loss: 193.5888   val Loss: 235.2846   time: 3.23s   best: 230.0701\n",
      "Epoch 894/2000:  train Loss: 194.0083   val Loss: 237.0065   time: 3.22s   best: 230.0701\n",
      "Epoch 895/2000:  train Loss: 193.4275   val Loss: 232.9936   time: 3.32s   best: 230.0701\n",
      "Epoch 896/2000:  train Loss: 193.3467   val Loss: 234.5195   time: 3.31s   best: 230.0701\n",
      "Epoch 897/2000:  train Loss: 194.1624   val Loss: 235.1556   time: 3.24s   best: 230.0701\n",
      "Epoch 898/2000:  train Loss: 194.3442   val Loss: 234.7162   time: 3.24s   best: 230.0701\n",
      "Epoch 899/2000:  train Loss: 193.0799   val Loss: 234.1014   time: 3.23s   best: 230.0701\n",
      "Epoch 900/2000:  train Loss: 193.7541   val Loss: 240.2300   time: 3.23s   best: 230.0701\n",
      "Epoch 901/2000:  train Loss: 194.3993   val Loss: 232.5169   time: 3.22s   best: 230.0701\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 902/2000:  train Loss: 193.6421   val Loss: 236.8644   time: 3.26s   best: 230.0701\n",
      "Epoch 903/2000:  train Loss: 193.5898   val Loss: 236.3511   time: 3.36s   best: 230.0701\n",
      "Epoch 904/2000:  train Loss: 195.8221   val Loss: 239.1333   time: 3.25s   best: 230.0701\n",
      "Epoch 905/2000:  train Loss: 193.9919   val Loss: 232.9549   time: 3.23s   best: 230.0701\n",
      "Epoch 906/2000:  train Loss: 192.0866   val Loss: 231.5094   time: 3.24s   best: 230.0701\n",
      "Epoch 907/2000:  train Loss: 194.9228   val Loss: 254.6804   time: 3.23s   best: 230.0701\n",
      "Epoch 908/2000:  train Loss: 198.8100   val Loss: 230.8104   time: 3.21s   best: 230.0701\n",
      "Epoch 909/2000:  train Loss: 194.2108   val Loss: 231.1642   time: 3.13s   best: 230.0701\n",
      "Epoch 910/2000:  train Loss: 192.7058   val Loss: 230.5865   time: 3.18s   best: 230.0701\n",
      "Epoch 911/2000:  train Loss: 193.3360   val Loss: 233.2492   time: 3.16s   best: 230.0701\n",
      "Epoch 912/2000:  train Loss: 194.5111   val Loss: 232.2090   time: 3.10s   best: 230.0701\n",
      "Epoch 913/2000:  train Loss: 193.2824   val Loss: 232.2944   time: 3.11s   best: 230.0701\n",
      "Epoch 914/2000:  train Loss: 193.1628   val Loss: 230.5078   time: 3.11s   best: 230.0701\n",
      "Epoch 915/2000:  train Loss: 192.4540   val Loss: 229.8031   time: 3.09s   best: 229.8031\n",
      "Epoch 916/2000:  train Loss: 193.3883   val Loss: 236.2361   time: 3.09s   best: 229.8031\n",
      "Epoch 917/2000:  train Loss: 193.2294   val Loss: 231.3289   time: 3.10s   best: 229.8031\n",
      "Epoch 918/2000:  train Loss: 191.7284   val Loss: 230.3866   time: 3.19s   best: 229.8031\n",
      "Epoch 919/2000:  train Loss: 192.2446   val Loss: 231.8005   time: 3.14s   best: 229.8031\n",
      "Epoch 920/2000:  train Loss: 193.5026   val Loss: 233.1243   time: 3.10s   best: 229.8031\n",
      "Epoch 921/2000:  train Loss: 194.1414   val Loss: 234.6980   time: 3.10s   best: 229.8031\n",
      "Epoch 922/2000:  train Loss: 192.0902   val Loss: 231.4909   time: 3.10s   best: 229.8031\n",
      "Epoch 923/2000:  train Loss: 194.9546   val Loss: 233.7727   time: 3.09s   best: 229.8031\n",
      "Epoch 924/2000:  train Loss: 192.9910   val Loss: 232.3945   time: 3.09s   best: 229.8031\n",
      "Epoch 925/2000:  train Loss: 192.8726   val Loss: 232.8676   time: 3.12s   best: 229.8031\n",
      "Epoch 926/2000:  train Loss: 192.2331   val Loss: 229.8011   time: 3.20s   best: 229.8011\n",
      "Epoch 927/2000:  train Loss: 192.6275   val Loss: 233.1363   time: 3.11s   best: 229.8011\n",
      "Epoch 928/2000:  train Loss: 191.3319   val Loss: 229.9298   time: 3.10s   best: 229.8011\n",
      "Epoch 929/2000:  train Loss: 192.7395   val Loss: 233.7472   time: 3.10s   best: 229.8011\n",
      "Epoch 930/2000:  train Loss: 192.8448   val Loss: 233.3567   time: 3.09s   best: 229.8011\n",
      "Epoch 931/2000:  train Loss: 192.8479   val Loss: 232.9498   time: 3.09s   best: 229.8011\n",
      "Epoch 932/2000:  train Loss: 191.2478   val Loss: 231.1294   time: 3.09s   best: 229.8011\n",
      "Epoch 933/2000:  train Loss: 192.7433   val Loss: 230.2137   time: 3.17s   best: 229.8011\n",
      "Epoch 934/2000:  train Loss: 195.1614   val Loss: 232.6306   time: 3.16s   best: 229.8011\n",
      "Epoch 935/2000:  train Loss: 195.7924   val Loss: 233.6344   time: 3.10s   best: 229.8011\n",
      "Epoch 936/2000:  train Loss: 191.7513   val Loss: 228.7466   time: 3.10s   best: 228.7466\n",
      "Epoch 937/2000:  train Loss: 192.5569   val Loss: 231.8706   time: 3.10s   best: 228.7466\n",
      "Epoch 938/2000:  train Loss: 191.5247   val Loss: 229.1346   time: 3.09s   best: 228.7466\n",
      "Epoch 939/2000:  train Loss: 191.3260   val Loss: 231.7799   time: 3.09s   best: 228.7466\n",
      "Epoch 940/2000:  train Loss: 191.4927   val Loss: 238.9121   time: 3.11s   best: 228.7466\n",
      "Epoch 941/2000:  train Loss: 192.6777   val Loss: 229.7742   time: 3.19s   best: 228.7466\n",
      "Epoch 942/2000:  train Loss: 192.1298   val Loss: 233.6054   time: 3.14s   best: 228.7466\n",
      "Epoch 943/2000:  train Loss: 191.6805   val Loss: 230.3464   time: 3.10s   best: 228.7466\n",
      "Epoch 944/2000:  train Loss: 191.9009   val Loss: 234.5306   time: 3.10s   best: 228.7466\n",
      "Epoch 945/2000:  train Loss: 191.5562   val Loss: 230.8741   time: 3.10s   best: 228.7466\n",
      "Epoch 946/2000:  train Loss: 191.0530   val Loss: 231.1796   time: 3.09s   best: 228.7466\n",
      "Epoch 947/2000:  train Loss: 190.5130   val Loss: 232.1009   time: 3.09s   best: 228.7466\n",
      "Epoch 948/2000:  train Loss: 193.2109   val Loss: 227.6519   time: 3.12s   best: 227.6519\n",
      "Epoch 949/2000:  train Loss: 191.1313   val Loss: 229.2832   time: 3.20s   best: 227.6519\n",
      "Epoch 950/2000:  train Loss: 191.2003   val Loss: 232.1598   time: 3.11s   best: 227.6519\n",
      "Epoch 951/2000:  train Loss: 191.4588   val Loss: 230.8074   time: 3.10s   best: 227.6519\n",
      "Epoch 952/2000:  train Loss: 189.8965   val Loss: 229.5685   time: 3.10s   best: 227.6519\n",
      "Epoch 953/2000:  train Loss: 191.1314   val Loss: 231.9352   time: 3.09s   best: 227.6519\n",
      "Epoch 954/2000:  train Loss: 195.1523   val Loss: 230.5281   time: 3.09s   best: 227.6519\n",
      "Epoch 955/2000:  train Loss: 193.6592   val Loss: 234.0320   time: 3.09s   best: 227.6519\n",
      "Epoch 956/2000:  train Loss: 193.6726   val Loss: 227.9052   time: 3.16s   best: 227.6519\n",
      "Epoch 957/2000:  train Loss: 191.5920   val Loss: 226.9593   time: 3.17s   best: 226.9593\n",
      "Epoch 958/2000:  train Loss: 191.2893   val Loss: 228.5338   time: 3.11s   best: 226.9593\n",
      "Epoch 959/2000:  train Loss: 190.7890   val Loss: 228.2296   time: 3.10s   best: 226.9593\n",
      "Epoch 960/2000:  train Loss: 191.5685   val Loss: 228.7709   time: 3.10s   best: 226.9593\n",
      "Epoch 961/2000:  train Loss: 189.8997   val Loss: 230.1547   time: 3.09s   best: 226.9593\n",
      "Epoch 962/2000:  train Loss: 192.6294   val Loss: 227.4726   time: 3.09s   best: 226.9593\n",
      "Epoch 963/2000:  train Loss: 190.1171   val Loss: 229.0638   time: 3.08s   best: 226.9593\n",
      "Epoch 964/2000:  train Loss: 190.9295   val Loss: 228.0776   time: 3.17s   best: 226.9593\n",
      "Epoch 965/2000:  train Loss: 189.3754   val Loss: 229.9795   time: 3.15s   best: 226.9593\n",
      "Epoch 966/2000:  train Loss: 191.3733   val Loss: 229.8313   time: 3.09s   best: 226.9593\n",
      "Epoch 967/2000:  train Loss: 190.6796   val Loss: 228.2984   time: 3.10s   best: 226.9593\n",
      "Epoch 968/2000:  train Loss: 190.0298   val Loss: 228.1070   time: 3.09s   best: 226.9593\n",
      "Epoch 969/2000:  train Loss: 189.6264   val Loss: 230.8797   time: 3.08s   best: 226.9593\n",
      "Epoch 970/2000:  train Loss: 191.9825   val Loss: 237.4449   time: 3.09s   best: 226.9593\n",
      "Epoch 971/2000:  train Loss: 191.9101   val Loss: 228.7032   time: 3.11s   best: 226.9593\n",
      "Epoch 972/2000:  train Loss: 190.1518   val Loss: 228.1217   time: 3.20s   best: 226.9593\n",
      "Epoch 973/2000:  train Loss: 189.5857   val Loss: 234.4110   time: 3.12s   best: 226.9593\n",
      "Epoch 974/2000:  train Loss: 190.1045   val Loss: 228.5626   time: 3.10s   best: 226.9593\n",
      "Epoch 975/2000:  train Loss: 189.2054   val Loss: 228.8799   time: 3.10s   best: 226.9593\n",
      "Epoch 976/2000:  train Loss: 190.3714   val Loss: 233.9668   time: 3.09s   best: 226.9593\n",
      "Epoch 977/2000:  train Loss: 190.0365   val Loss: 229.5918   time: 3.09s   best: 226.9593\n",
      "Epoch 978/2000:  train Loss: 193.2847   val Loss: 233.4391   time: 3.08s   best: 226.9593\n",
      "Epoch 979/2000:  train Loss: 191.8531   val Loss: 227.4627   time: 3.13s   best: 226.9593\n",
      "Epoch 980/2000:  train Loss: 189.8810   val Loss: 228.6976   time: 3.18s   best: 226.9593\n",
      "Epoch 981/2000:  train Loss: 188.9644   val Loss: 229.3679   time: 3.11s   best: 226.9593\n",
      "Epoch 982/2000:  train Loss: 189.5573   val Loss: 228.8170   time: 3.10s   best: 226.9593\n",
      "Epoch 983/2000:  train Loss: 190.0200   val Loss: 240.4394   time: 3.10s   best: 226.9593\n",
      "Epoch 984/2000:  train Loss: 192.1108   val Loss: 231.6113   time: 3.09s   best: 226.9593\n",
      "Epoch 985/2000:  train Loss: 189.9209   val Loss: 229.5814   time: 3.09s   best: 226.9593\n",
      "Epoch 986/2000:  train Loss: 189.0833   val Loss: 228.5967   time: 3.09s   best: 226.9593\n",
      "Epoch 987/2000:  train Loss: 189.7144   val Loss: 228.4306   time: 3.15s   best: 226.9593\n",
      "Epoch 988/2000:  train Loss: 189.2780   val Loss: 230.6564   time: 3.17s   best: 226.9593\n",
      "Epoch 989/2000:  train Loss: 189.2217   val Loss: 228.4653   time: 3.10s   best: 226.9593\n",
      "Epoch 990/2000:  train Loss: 189.3488   val Loss: 230.2966   time: 3.10s   best: 226.9593\n",
      "Epoch 991/2000:  train Loss: 188.7709   val Loss: 229.2274   time: 3.10s   best: 226.9593\n",
      "Epoch 992/2000:  train Loss: 189.7226   val Loss: 231.4139   time: 3.09s   best: 226.9593\n",
      "Epoch 993/2000:  train Loss: 190.2958   val Loss: 228.2045   time: 3.09s   best: 226.9593\n",
      "Epoch 994/2000:  train Loss: 189.4130   val Loss: 231.3261   time: 3.09s   best: 226.9593\n",
      "Epoch 995/2000:  train Loss: 191.7375   val Loss: 229.1150   time: 3.18s   best: 226.9593\n",
      "Epoch 996/2000:  train Loss: 188.1596   val Loss: 226.8157   time: 3.14s   best: 226.8157\n",
      "Epoch 997/2000:  train Loss: 188.3588   val Loss: 231.1291   time: 3.10s   best: 226.8157\n",
      "Epoch 998/2000:  train Loss: 188.3893   val Loss: 227.6544   time: 3.10s   best: 226.8157\n",
      "Epoch 999/2000:  train Loss: 191.4185   val Loss: 232.0344   time: 3.10s   best: 226.8157\n",
      "Epoch 1000/2000:  train Loss: 191.8779   val Loss: 227.8588   time: 3.09s   best: 226.8157\n",
      "Epoch 1001/2000:  train Loss: 188.9303   val Loss: 233.4632   time: 3.09s   best: 226.8157\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1002/2000:  train Loss: 188.8339   val Loss: 228.1713   time: 3.13s   best: 226.8157\n",
      "Epoch 1003/2000:  train Loss: 188.1209   val Loss: 228.5102   time: 3.19s   best: 226.8157\n",
      "Epoch 1004/2000:  train Loss: 187.9310   val Loss: 230.4148   time: 3.12s   best: 226.8157\n",
      "Epoch 1005/2000:  train Loss: 190.2127   val Loss: 228.4908   time: 3.10s   best: 226.8157\n",
      "Epoch 1006/2000:  train Loss: 187.3478   val Loss: 228.7960   time: 3.10s   best: 226.8157\n",
      "Epoch 1007/2000:  train Loss: 188.2470   val Loss: 228.1559   time: 3.09s   best: 226.8157\n",
      "Epoch 1008/2000:  train Loss: 188.6282   val Loss: 228.4323   time: 3.09s   best: 226.8157\n",
      "Epoch 1009/2000:  train Loss: 192.1170   val Loss: 253.3458   time: 3.09s   best: 226.8157\n",
      "Epoch 1010/2000:  train Loss: 195.1195   val Loss: 228.4222   time: 3.16s   best: 226.8157\n",
      "Epoch 1011/2000:  train Loss: 189.1401   val Loss: 230.6177   time: 3.17s   best: 226.8157\n",
      "Epoch 1012/2000:  train Loss: 189.7037   val Loss: 228.3075   time: 3.10s   best: 226.8157\n",
      "Epoch 1013/2000:  train Loss: 188.4164   val Loss: 225.9055   time: 3.10s   best: 225.9055\n",
      "Epoch 1014/2000:  train Loss: 187.0939   val Loss: 226.1617   time: 3.10s   best: 225.9055\n",
      "Epoch 1015/2000:  train Loss: 188.0246   val Loss: 227.9589   time: 3.09s   best: 225.9055\n",
      "Epoch 1016/2000:  train Loss: 189.7522   val Loss: 232.2983   time: 3.09s   best: 225.9055\n",
      "Epoch 1017/2000:  train Loss: 189.7894   val Loss: 226.2889   time: 3.09s   best: 225.9055\n",
      "Epoch 1018/2000:  train Loss: 187.4954   val Loss: 225.9284   time: 3.18s   best: 225.9055\n",
      "Epoch 1019/2000:  train Loss: 187.1057   val Loss: 225.5080   time: 3.14s   best: 225.5080\n",
      "Epoch 1020/2000:  train Loss: 187.5157   val Loss: 227.6106   time: 3.10s   best: 225.5080\n",
      "Epoch 1021/2000:  train Loss: 187.5630   val Loss: 225.5514   time: 3.10s   best: 225.5080\n",
      "Epoch 1022/2000:  train Loss: 188.1021   val Loss: 228.5649   time: 3.10s   best: 225.5080\n",
      "Epoch 1023/2000:  train Loss: 187.9989   val Loss: 227.6107   time: 3.09s   best: 225.5080\n",
      "Epoch 1024/2000:  train Loss: 187.2864   val Loss: 228.4630   time: 3.08s   best: 225.5080\n",
      "Epoch 1025/2000:  train Loss: 188.3551   val Loss: 228.0916   time: 3.13s   best: 225.5080\n",
      "Epoch 1026/2000:  train Loss: 187.8679   val Loss: 228.2451   time: 3.19s   best: 225.5080\n",
      "Epoch 1027/2000:  train Loss: 187.6367   val Loss: 229.8025   time: 3.12s   best: 225.5080\n",
      "Epoch 1028/2000:  train Loss: 187.6292   val Loss: 229.2302   time: 3.11s   best: 225.5080\n",
      "Epoch 1029/2000:  train Loss: 188.6821   val Loss: 231.4981   time: 3.10s   best: 225.5080\n",
      "Epoch 1030/2000:  train Loss: 187.9265   val Loss: 228.7558   time: 3.09s   best: 225.5080\n",
      "Epoch 1031/2000:  train Loss: 187.8391   val Loss: 228.4653   time: 3.09s   best: 225.5080\n",
      "Epoch 1032/2000:  train Loss: 187.9951   val Loss: 226.2742   time: 3.08s   best: 225.5080\n",
      "Epoch 1033/2000:  train Loss: 187.2537   val Loss: 229.0586   time: 3.14s   best: 225.5080\n",
      "Epoch 1034/2000:  train Loss: 188.0049   val Loss: 227.5816   time: 3.18s   best: 225.5080\n",
      "Epoch 1035/2000:  train Loss: 187.2810   val Loss: 227.8641   time: 3.11s   best: 225.5080\n",
      "Epoch 1036/2000:  train Loss: 187.7537   val Loss: 230.7650   time: 3.10s   best: 225.5080\n",
      "Epoch 1037/2000:  train Loss: 186.1572   val Loss: 227.9278   time: 3.10s   best: 225.5080\n",
      "Epoch 1038/2000:  train Loss: 186.9384   val Loss: 227.7281   time: 3.10s   best: 225.5080\n",
      "Epoch 1039/2000:  train Loss: 186.8134   val Loss: 229.2216   time: 3.09s   best: 225.5080\n",
      "Epoch 1040/2000:  train Loss: 187.7162   val Loss: 229.9838   time: 3.09s   best: 225.5080\n",
      "Epoch 1041/2000:  train Loss: 187.2175   val Loss: 226.5401   time: 3.16s   best: 225.5080\n",
      "Epoch 1042/2000:  train Loss: 187.1606   val Loss: 230.8948   time: 3.16s   best: 225.5080\n",
      "Epoch 1043/2000:  train Loss: 188.9235   val Loss: 229.2484   time: 3.10s   best: 225.5080\n",
      "Epoch 1044/2000:  train Loss: 187.9801   val Loss: 228.1000   time: 3.10s   best: 225.5080\n",
      "Epoch 1045/2000:  train Loss: 186.5187   val Loss: 227.9873   time: 3.10s   best: 225.5080\n",
      "Epoch 1046/2000:  train Loss: 187.1946   val Loss: 229.3224   time: 3.09s   best: 225.5080\n",
      "Epoch 1047/2000:  train Loss: 187.3728   val Loss: 226.8653   time: 3.09s   best: 225.5080\n",
      "Epoch 1048/2000:  train Loss: 187.1391   val Loss: 233.7048   time: 3.10s   best: 225.5080\n",
      "Epoch 1049/2000:  train Loss: 186.1551   val Loss: 229.8392   time: 3.20s   best: 225.5080\n",
      "Epoch 1050/2000:  train Loss: 186.2347   val Loss: 229.2612   time: 3.13s   best: 225.5080\n",
      "Epoch 1051/2000:  train Loss: 186.7154   val Loss: 228.7644   time: 3.10s   best: 225.5080\n",
      "Epoch 1052/2000:  train Loss: 187.4364   val Loss: 228.1323   time: 3.10s   best: 225.5080\n",
      "Epoch 1053/2000:  train Loss: 186.1834   val Loss: 230.3792   time: 3.10s   best: 225.5080\n",
      "Epoch 1054/2000:  train Loss: 187.9163   val Loss: 233.5703   time: 3.09s   best: 225.5080\n",
      "Epoch 1055/2000:  train Loss: 189.4399   val Loss: 232.7568   time: 3.08s   best: 225.5080\n",
      "Epoch 1056/2000:  train Loss: 190.9694   val Loss: 228.6262   time: 3.12s   best: 225.5080\n",
      "Epoch 1057/2000:  train Loss: 187.8847   val Loss: 228.5557   time: 3.19s   best: 225.5080\n",
      "Epoch 1058/2000:  train Loss: 186.2600   val Loss: 226.2343   time: 3.11s   best: 225.5080\n",
      "Epoch 1059/2000:  train Loss: 185.9591   val Loss: 227.4198   time: 3.10s   best: 225.5080\n",
      "Epoch 1060/2000:  train Loss: 185.6495   val Loss: 229.1584   time: 3.10s   best: 225.5080\n",
      "Epoch 1061/2000:  train Loss: 185.9777   val Loss: 227.4854   time: 3.09s   best: 225.5080\n",
      "Epoch 1062/2000:  train Loss: 186.4068   val Loss: 225.3226   time: 3.09s   best: 225.3226\n",
      "Epoch 1063/2000:  train Loss: 187.1081   val Loss: 227.7161   time: 3.09s   best: 225.3226\n",
      "Epoch 1064/2000:  train Loss: 189.4428   val Loss: 226.8756   time: 3.16s   best: 225.3226\n",
      "Epoch 1065/2000:  train Loss: 186.1416   val Loss: 227.2273   time: 3.17s   best: 225.3226\n",
      "Epoch 1066/2000:  train Loss: 185.1701   val Loss: 227.4425   time: 3.10s   best: 225.3226\n",
      "Epoch 1067/2000:  train Loss: 186.5814   val Loss: 226.4351   time: 3.10s   best: 225.3226\n",
      "Epoch 1068/2000:  train Loss: 188.1784   val Loss: 231.9996   time: 3.10s   best: 225.3226\n",
      "Epoch 1069/2000:  train Loss: 185.7561   val Loss: 226.8873   time: 3.09s   best: 225.3226\n",
      "Epoch 1070/2000:  train Loss: 185.3240   val Loss: 228.4421   time: 3.09s   best: 225.3226\n",
      "Epoch 1071/2000:  train Loss: 186.6325   val Loss: 228.9352   time: 3.10s   best: 225.3226\n",
      "Epoch 1072/2000:  train Loss: 186.1353   val Loss: 227.8178   time: 3.20s   best: 225.3226\n",
      "Epoch 1073/2000:  train Loss: 185.4698   val Loss: 230.3051   time: 3.13s   best: 225.3226\n",
      "Epoch 1074/2000:  train Loss: 186.4723   val Loss: 225.6397   time: 3.10s   best: 225.3226\n",
      "Epoch 1075/2000:  train Loss: 184.9174   val Loss: 228.6816   time: 3.10s   best: 225.3226\n",
      "Epoch 1076/2000:  train Loss: 185.5385   val Loss: 227.7423   time: 3.10s   best: 225.3226\n",
      "Epoch 1077/2000:  train Loss: 185.5826   val Loss: 228.5713   time: 3.08s   best: 225.3226\n",
      "Epoch 1078/2000:  train Loss: 186.2650   val Loss: 231.0071   time: 3.08s   best: 225.3226\n",
      "Epoch 1079/2000:  train Loss: 186.7071   val Loss: 228.5003   time: 3.11s   best: 225.3226\n",
      "Epoch 1080/2000:  train Loss: 185.3363   val Loss: 229.9769   time: 3.17s   best: 225.3226\n",
      "Epoch 1081/2000:  train Loss: 185.6767   val Loss: 228.4177   time: 3.11s   best: 225.3226\n",
      "Epoch 1082/2000:  train Loss: 185.3457   val Loss: 229.5291   time: 3.10s   best: 225.3226\n",
      "Epoch 1083/2000:  train Loss: 185.5469   val Loss: 227.6158   time: 3.10s   best: 225.3226\n",
      "Epoch 1084/2000:  train Loss: 186.4426   val Loss: 227.5386   time: 3.09s   best: 225.3226\n",
      "Epoch 1085/2000:  train Loss: 185.3263   val Loss: 246.6114   time: 3.08s   best: 225.3226\n",
      "Epoch 1086/2000:  train Loss: 187.4768   val Loss: 227.5318   time: 3.08s   best: 225.3226\n",
      "Epoch 1087/2000:  train Loss: 184.6765   val Loss: 229.6353   time: 3.13s   best: 225.3226\n",
      "Epoch 1088/2000:  train Loss: 184.4181   val Loss: 226.5541   time: 3.18s   best: 225.3226\n",
      "Epoch 1089/2000:  train Loss: 184.4700   val Loss: 229.5998   time: 3.10s   best: 225.3226\n",
      "Epoch 1090/2000:  train Loss: 185.5899   val Loss: 229.2835   time: 3.10s   best: 225.3226\n",
      "Epoch 1091/2000:  train Loss: 184.1079   val Loss: 225.9728   time: 3.10s   best: 225.3226\n",
      "Epoch 1092/2000:  train Loss: 186.2268   val Loss: 228.2717   time: 3.09s   best: 225.3226\n",
      "Epoch 1093/2000:  train Loss: 186.9210   val Loss: 226.5865   time: 3.09s   best: 225.3226\n",
      "Epoch 1094/2000:  train Loss: 184.1297   val Loss: 225.4951   time: 3.09s   best: 225.3226\n",
      "Epoch 1095/2000:  train Loss: 184.4581   val Loss: 226.7424   time: 3.17s   best: 225.3226\n",
      "Epoch 1096/2000:  train Loss: 184.2508   val Loss: 225.3814   time: 3.15s   best: 225.3226\n",
      "Epoch 1097/2000:  train Loss: 184.7379   val Loss: 226.5320   time: 3.10s   best: 225.3226\n",
      "Epoch 1098/2000:  train Loss: 184.9342   val Loss: 228.1589   time: 3.10s   best: 225.3226\n",
      "Epoch 1099/2000:  train Loss: 184.8632   val Loss: 226.1203   time: 3.10s   best: 225.3226\n",
      "Epoch 1100/2000:  train Loss: 184.9984   val Loss: 229.1535   time: 3.09s   best: 225.3226\n",
      "Epoch 1101/2000:  train Loss: 185.0872   val Loss: 225.7119   time: 3.09s   best: 225.3226\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1102/2000:  train Loss: 183.9789   val Loss: 224.4765   time: 3.11s   best: 224.4765\n",
      "Epoch 1103/2000:  train Loss: 183.9804   val Loss: 228.0874   time: 3.19s   best: 224.4765\n",
      "Epoch 1104/2000:  train Loss: 183.7225   val Loss: 226.1166   time: 3.12s   best: 224.4765\n",
      "Epoch 1105/2000:  train Loss: 183.5525   val Loss: 224.7064   time: 3.10s   best: 224.4765\n",
      "Epoch 1106/2000:  train Loss: 184.6287   val Loss: 226.3755   time: 3.10s   best: 224.4765\n",
      "Epoch 1107/2000:  train Loss: 185.4403   val Loss: 225.6501   time: 3.10s   best: 224.4765\n",
      "Epoch 1108/2000:  train Loss: 185.3231   val Loss: 225.5843   time: 3.09s   best: 224.4765\n",
      "Epoch 1109/2000:  train Loss: 185.0094   val Loss: 226.2858   time: 3.09s   best: 224.4765\n",
      "Epoch 1110/2000:  train Loss: 184.7429   val Loss: 227.3894   time: 3.13s   best: 224.4765\n",
      "Epoch 1111/2000:  train Loss: 186.2879   val Loss: 225.0967   time: 3.19s   best: 224.4765\n",
      "Epoch 1112/2000:  train Loss: 185.6321   val Loss: 226.8058   time: 3.11s   best: 224.4765\n",
      "Epoch 1113/2000:  train Loss: 183.7691   val Loss: 225.0839   time: 3.10s   best: 224.4765\n",
      "Epoch 1114/2000:  train Loss: 184.4178   val Loss: 227.5145   time: 3.10s   best: 224.4765\n",
      "Epoch 1115/2000:  train Loss: 183.6720   val Loss: 229.8148   time: 3.10s   best: 224.4765\n",
      "Epoch 1116/2000:  train Loss: 184.3388   val Loss: 224.7511   time: 3.09s   best: 224.4765\n",
      "Epoch 1117/2000:  train Loss: 183.8355   val Loss: 226.7536   time: 3.09s   best: 224.4765\n",
      "Epoch 1118/2000:  train Loss: 183.4676   val Loss: 226.1210   time: 3.16s   best: 224.4765\n",
      "Epoch 1119/2000:  train Loss: 183.9263   val Loss: 229.0034   time: 3.17s   best: 224.4765\n",
      "Epoch 1120/2000:  train Loss: 183.8711   val Loss: 224.8104   time: 3.11s   best: 224.4765\n",
      "Epoch 1121/2000:  train Loss: 183.2884   val Loss: 225.9547   time: 3.10s   best: 224.4765\n",
      "Epoch 1122/2000:  train Loss: 184.0189   val Loss: 226.7766   time: 3.10s   best: 224.4765\n",
      "Epoch 1123/2000:  train Loss: 184.4520   val Loss: 229.6536   time: 3.09s   best: 224.4765\n",
      "Epoch 1124/2000:  train Loss: 184.7022   val Loss: 225.9078   time: 3.09s   best: 224.4765\n",
      "Epoch 1125/2000:  train Loss: 183.0683   val Loss: 226.1031   time: 3.10s   best: 224.4765\n",
      "Epoch 1126/2000:  train Loss: 183.0095   val Loss: 223.3998   time: 3.18s   best: 223.3998\n",
      "Epoch 1127/2000:  train Loss: 183.0841   val Loss: 225.0299   time: 3.15s   best: 223.3998\n",
      "Epoch 1128/2000:  train Loss: 182.6981   val Loss: 229.2657   time: 3.10s   best: 223.3998\n",
      "Epoch 1129/2000:  train Loss: 183.8426   val Loss: 226.6429   time: 3.10s   best: 223.3998\n",
      "Epoch 1130/2000:  train Loss: 183.1154   val Loss: 224.3780   time: 3.09s   best: 223.3998\n",
      "Epoch 1131/2000:  train Loss: 184.7707   val Loss: 225.9558   time: 3.09s   best: 223.3998\n",
      "Epoch 1132/2000:  train Loss: 184.0526   val Loss: 227.4824   time: 3.08s   best: 223.3998\n",
      "Epoch 1133/2000:  train Loss: 183.2746   val Loss: 227.1300   time: 3.11s   best: 223.3998\n",
      "Epoch 1134/2000:  train Loss: 183.0603   val Loss: 228.1166   time: 3.18s   best: 223.3998\n",
      "Epoch 1135/2000:  train Loss: 188.0919   val Loss: 224.8606   time: 3.11s   best: 223.3998\n",
      "Epoch 1136/2000:  train Loss: 183.1631   val Loss: 228.1845   time: 3.10s   best: 223.3998\n",
      "Epoch 1137/2000:  train Loss: 183.2102   val Loss: 226.5793   time: 3.10s   best: 223.3998\n",
      "Epoch 1138/2000:  train Loss: 183.3178   val Loss: 228.8265   time: 3.09s   best: 223.3998\n",
      "Epoch 1139/2000:  train Loss: 183.3006   val Loss: 228.8028   time: 3.09s   best: 223.3998\n",
      "Epoch 1140/2000:  train Loss: 182.3563   val Loss: 226.1151   time: 3.09s   best: 223.3998\n",
      "Epoch 1141/2000:  train Loss: 182.9576   val Loss: 233.4303   time: 3.14s   best: 223.3998\n",
      "Epoch 1142/2000:  train Loss: 184.4311   val Loss: 227.4365   time: 3.18s   best: 223.3998\n",
      "Epoch 1143/2000:  train Loss: 183.0301   val Loss: 229.2941   time: 3.10s   best: 223.3998\n",
      "Epoch 1144/2000:  train Loss: 183.3089   val Loss: 226.3273   time: 3.10s   best: 223.3998\n",
      "Epoch 1145/2000:  train Loss: 181.8003   val Loss: 225.7286   time: 3.10s   best: 223.3998\n",
      "Epoch 1146/2000:  train Loss: 182.1637   val Loss: 227.4393   time: 3.09s   best: 223.3998\n",
      "Epoch 1147/2000:  train Loss: 184.5316   val Loss: 225.8837   time: 3.09s   best: 223.3998\n",
      "Epoch 1148/2000:  train Loss: 181.7954   val Loss: 226.2817   time: 3.09s   best: 223.3998\n",
      "Epoch 1149/2000:  train Loss: 182.0192   val Loss: 225.6692   time: 3.17s   best: 223.3998\n",
      "Epoch 1150/2000:  train Loss: 182.1037   val Loss: 225.5768   time: 3.15s   best: 223.3998\n",
      "Epoch 1151/2000:  train Loss: 183.7730   val Loss: 256.5898   time: 3.10s   best: 223.3998\n",
      "Epoch 1152/2000:  train Loss: 189.1929   val Loss: 225.9832   time: 3.10s   best: 223.3998\n",
      "Epoch 1153/2000:  train Loss: 182.6828   val Loss: 228.1805   time: 3.10s   best: 223.3998\n",
      "Epoch 1154/2000:  train Loss: 182.6442   val Loss: 228.9092   time: 3.09s   best: 223.3998\n",
      "Epoch 1155/2000:  train Loss: 183.4101   val Loss: 225.8827   time: 3.09s   best: 223.3998\n",
      "Epoch 1156/2000:  train Loss: 182.0016   val Loss: 225.7737   time: 3.10s   best: 223.3998\n",
      "Epoch 1157/2000:  train Loss: 181.5119   val Loss: 226.7228   time: 3.18s   best: 223.3998\n",
      "Epoch 1158/2000:  train Loss: 181.6447   val Loss: 225.7896   time: 3.14s   best: 223.3998\n",
      "Epoch 1159/2000:  train Loss: 182.6383   val Loss: 226.3762   time: 3.10s   best: 223.3998\n",
      "Epoch 1160/2000:  train Loss: 181.7152   val Loss: 224.8569   time: 3.10s   best: 223.3998\n",
      "Epoch 1161/2000:  train Loss: 181.6328   val Loss: 226.1843   time: 3.10s   best: 223.3998\n",
      "Epoch 1162/2000:  train Loss: 182.3620   val Loss: 227.2982   time: 3.09s   best: 223.3998\n",
      "Epoch 1163/2000:  train Loss: 182.1414   val Loss: 226.1024   time: 3.09s   best: 223.3998\n",
      "Epoch 1164/2000:  train Loss: 182.2459   val Loss: 228.8901   time: 3.12s   best: 223.3998\n",
      "Epoch 1165/2000:  train Loss: 185.6937   val Loss: 239.0617   time: 3.19s   best: 223.3998\n",
      "Epoch 1166/2000:  train Loss: 184.5819   val Loss: 224.8241   time: 3.11s   best: 223.3998\n",
      "Epoch 1167/2000:  train Loss: 181.5589   val Loss: 229.1954   time: 3.10s   best: 223.3998\n",
      "Epoch 1168/2000:  train Loss: 184.1834   val Loss: 225.4127   time: 3.10s   best: 223.3998\n",
      "Epoch 1169/2000:  train Loss: 181.0562   val Loss: 226.7889   time: 3.09s   best: 223.3998\n",
      "Epoch 1170/2000:  train Loss: 180.7972   val Loss: 224.4980   time: 3.09s   best: 223.3998\n",
      "Epoch 1171/2000:  train Loss: 180.5352   val Loss: 227.3546   time: 3.09s   best: 223.3998\n",
      "Epoch 1172/2000:  train Loss: 186.7635   val Loss: 233.0533   time: 3.16s   best: 223.3998\n",
      "Epoch 1173/2000:  train Loss: 183.1394   val Loss: 227.6908   time: 3.16s   best: 223.3998\n",
      "Epoch 1174/2000:  train Loss: 182.1193   val Loss: 227.2592   time: 3.10s   best: 223.3998\n",
      "Epoch 1175/2000:  train Loss: 181.5151   val Loss: 226.6361   time: 3.10s   best: 223.3998\n",
      "Epoch 1176/2000:  train Loss: 180.7762   val Loss: 226.8161   time: 3.10s   best: 223.3998\n",
      "Epoch 1177/2000:  train Loss: 181.3161   val Loss: 227.6765   time: 3.09s   best: 223.3998\n",
      "Epoch 1178/2000:  train Loss: 182.3367   val Loss: 226.6699   time: 3.09s   best: 223.3998\n",
      "Epoch 1179/2000:  train Loss: 181.0305   val Loss: 227.3291   time: 3.09s   best: 223.3998\n",
      "Epoch 1180/2000:  train Loss: 182.1676   val Loss: 227.6612   time: 3.18s   best: 223.3998\n",
      "Epoch 1181/2000:  train Loss: 180.7902   val Loss: 229.9866   time: 3.15s   best: 223.3998\n",
      "Epoch 1182/2000:  train Loss: 180.9106   val Loss: 227.4531   time: 3.10s   best: 223.3998\n",
      "Epoch 1183/2000:  train Loss: 181.3521   val Loss: 229.6625   time: 3.10s   best: 223.3998\n",
      "Epoch 1184/2000:  train Loss: 184.4250   val Loss: 231.0533   time: 3.10s   best: 223.3998\n",
      "Epoch 1185/2000:  train Loss: 182.4472   val Loss: 225.8139   time: 3.08s   best: 223.3998\n",
      "Epoch 1186/2000:  train Loss: 180.8121   val Loss: 229.9003   time: 3.08s   best: 223.3998\n",
      "Epoch 1187/2000:  train Loss: 181.1001   val Loss: 227.4437   time: 3.11s   best: 223.3998\n",
      "Epoch 1188/2000:  train Loss: 181.4245   val Loss: 225.1084   time: 3.19s   best: 223.3998\n",
      "Epoch 1189/2000:  train Loss: 182.1535   val Loss: 227.0942   time: 3.13s   best: 223.3998\n",
      "Epoch 1190/2000:  train Loss: 180.1497   val Loss: 228.7922   time: 3.10s   best: 223.3998\n",
      "Epoch 1191/2000:  train Loss: 180.9266   val Loss: 227.1247   time: 3.09s   best: 223.3998\n",
      "Epoch 1192/2000:  train Loss: 183.8604   val Loss: 233.8801   time: 3.14s   best: 223.3998\n",
      "Epoch 1193/2000:  train Loss: 184.1203   val Loss: 225.9725   time: 3.09s   best: 223.3998\n",
      "Epoch 1194/2000:  train Loss: 183.0436   val Loss: 227.9493   time: 3.09s   best: 223.3998\n",
      "Epoch 1195/2000:  train Loss: 181.1680   val Loss: 229.9295   time: 3.13s   best: 223.3998\n",
      "Epoch 1196/2000:  train Loss: 182.7230   val Loss: 226.1672   time: 3.19s   best: 223.3998\n",
      "Epoch 1197/2000:  train Loss: 180.2549   val Loss: 227.3041   time: 3.11s   best: 223.3998\n",
      "Epoch 1198/2000:  train Loss: 180.8068   val Loss: 224.8783   time: 3.10s   best: 223.3998\n",
      "Epoch 1199/2000:  train Loss: 179.4416   val Loss: 225.3577   time: 3.10s   best: 223.3998\n",
      "Epoch 1200/2000:  train Loss: 180.0278   val Loss: 226.1597   time: 3.10s   best: 223.3998\n",
      "Epoch 1201/2000:  train Loss: 181.4812   val Loss: 228.4811   time: 3.09s   best: 223.3998\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1202/2000:  train Loss: 180.2677   val Loss: 226.4863   time: 3.09s   best: 223.3998\n",
      "Epoch 1203/2000:  train Loss: 180.6514   val Loss: 226.0378   time: 3.18s   best: 223.3998\n",
      "Epoch 1204/2000:  train Loss: 182.2525   val Loss: 224.5454   time: 3.15s   best: 223.3998\n",
      "Epoch 1205/2000:  train Loss: 182.1820   val Loss: 225.1491   time: 3.11s   best: 223.3998\n",
      "Epoch 1206/2000:  train Loss: 180.7218   val Loss: 227.0105   time: 3.11s   best: 223.3998\n",
      "Epoch 1207/2000:  train Loss: 180.0312   val Loss: 225.4259   time: 3.10s   best: 223.3998\n",
      "Epoch 1208/2000:  train Loss: 179.7488   val Loss: 225.7983   time: 3.09s   best: 223.3998\n",
      "Epoch 1209/2000:  train Loss: 180.5011   val Loss: 224.7163   time: 3.09s   best: 223.3998\n",
      "Epoch 1210/2000:  train Loss: 181.7279   val Loss: 226.0996   time: 3.10s   best: 223.3998\n",
      "Epoch 1211/2000:  train Loss: 181.3453   val Loss: 224.5509   time: 3.19s   best: 223.3998\n",
      "Epoch 1212/2000:  train Loss: 179.8276   val Loss: 224.3263   time: 3.14s   best: 223.3998\n",
      "Epoch 1213/2000:  train Loss: 179.6516   val Loss: 226.2769   time: 3.10s   best: 223.3998\n",
      "Epoch 1214/2000:  train Loss: 180.0444   val Loss: 226.5124   time: 3.10s   best: 223.3998\n",
      "Epoch 1215/2000:  train Loss: 179.6464   val Loss: 227.5608   time: 3.10s   best: 223.3998\n",
      "Epoch 1216/2000:  train Loss: 183.5684   val Loss: 231.5041   time: 3.10s   best: 223.3998\n",
      "Epoch 1217/2000:  train Loss: 181.2341   val Loss: 224.5589   time: 3.09s   best: 223.3998\n",
      "Epoch 1218/2000:  train Loss: 180.8945   val Loss: 224.1497   time: 3.12s   best: 223.3998\n",
      "Epoch 1219/2000:  train Loss: 180.4964   val Loss: 227.6433   time: 3.19s   best: 223.3998\n",
      "Epoch 1220/2000:  train Loss: 180.0792   val Loss: 226.7919   time: 3.13s   best: 223.3998\n",
      "Epoch 1221/2000:  train Loss: 179.5905   val Loss: 225.9058   time: 3.11s   best: 223.3998\n",
      "Epoch 1222/2000:  train Loss: 179.7943   val Loss: 224.2752   time: 3.10s   best: 223.3998\n",
      "Epoch 1223/2000:  train Loss: 180.3383   val Loss: 228.0733   time: 3.10s   best: 223.3998\n",
      "Epoch 1224/2000:  train Loss: 179.2694   val Loss: 226.7833   time: 3.10s   best: 223.3998\n",
      "Epoch 1225/2000:  train Loss: 180.1736   val Loss: 226.4675   time: 3.09s   best: 223.3998\n",
      "Epoch 1226/2000:  train Loss: 181.3514   val Loss: 225.2350   time: 3.13s   best: 223.3998\n",
      "Epoch 1227/2000:  train Loss: 179.6856   val Loss: 225.5371   time: 3.19s   best: 223.3998\n",
      "Epoch 1228/2000:  train Loss: 180.2148   val Loss: 227.5830   time: 3.12s   best: 223.3998\n",
      "Epoch 1229/2000:  train Loss: 179.4064   val Loss: 225.4732   time: 3.10s   best: 223.3998\n",
      "Epoch 1230/2000:  train Loss: 179.5131   val Loss: 227.6096   time: 3.10s   best: 223.3998\n",
      "Epoch 1231/2000:  train Loss: 179.7399   val Loss: 227.5416   time: 3.09s   best: 223.3998\n",
      "Epoch 1232/2000:  train Loss: 180.7325   val Loss: 226.7757   time: 3.09s   best: 223.3998\n",
      "Epoch 1233/2000:  train Loss: 178.9325   val Loss: 225.2028   time: 3.09s   best: 223.3998\n",
      "Epoch 1234/2000:  train Loss: 178.7269   val Loss: 224.5228   time: 3.15s   best: 223.3998\n",
      "Epoch 1235/2000:  train Loss: 179.6245   val Loss: 225.5170   time: 3.17s   best: 223.3998\n",
      "Epoch 1236/2000:  train Loss: 178.8420   val Loss: 225.0462   time: 3.10s   best: 223.3998\n",
      "Epoch 1237/2000:  train Loss: 179.8338   val Loss: 229.8630   time: 3.10s   best: 223.3998\n",
      "Epoch 1238/2000:  train Loss: 179.1912   val Loss: 225.6006   time: 3.10s   best: 223.3998\n",
      "Epoch 1239/2000:  train Loss: 179.4623   val Loss: 227.1859   time: 3.09s   best: 223.3998\n",
      "Epoch 1240/2000:  train Loss: 179.4576   val Loss: 225.6593   time: 3.09s   best: 223.3998\n",
      "Epoch 1241/2000:  train Loss: 179.7244   val Loss: 224.1349   time: 3.09s   best: 223.3998\n",
      "Epoch 1242/2000:  train Loss: 178.5654   val Loss: 225.2249   time: 3.18s   best: 223.3998\n",
      "Epoch 1243/2000:  train Loss: 178.4332   val Loss: 224.8001   time: 3.13s   best: 223.3998\n",
      "Epoch 1244/2000:  train Loss: 178.1939   val Loss: 226.1655   time: 3.10s   best: 223.3998\n",
      "Epoch 1245/2000:  train Loss: 179.2103   val Loss: 227.5828   time: 3.10s   best: 223.3998\n",
      "Epoch 1246/2000:  train Loss: 179.5833   val Loss: 228.5230   time: 3.09s   best: 223.3998\n",
      "Epoch 1247/2000:  train Loss: 180.1221   val Loss: 225.3052   time: 3.09s   best: 223.3998\n",
      "Epoch 1248/2000:  train Loss: 182.4386   val Loss: 225.3407   time: 3.09s   best: 223.3998\n",
      "Epoch 1249/2000:  train Loss: 179.9011   val Loss: 222.4015   time: 3.12s   best: 222.4015\n",
      "Epoch 1250/2000:  train Loss: 180.2603   val Loss: 224.2560   time: 3.18s   best: 222.4015\n",
      "Epoch 1251/2000:  train Loss: 178.7134   val Loss: 224.8162   time: 3.12s   best: 222.4015\n",
      "Epoch 1252/2000:  train Loss: 179.0305   val Loss: 227.0782   time: 3.09s   best: 222.4015\n",
      "Epoch 1253/2000:  train Loss: 178.5944   val Loss: 224.6981   time: 3.10s   best: 222.4015\n",
      "Epoch 1254/2000:  train Loss: 178.6135   val Loss: 226.7744   time: 3.09s   best: 222.4015\n",
      "Epoch 1255/2000:  train Loss: 179.7495   val Loss: 225.1700   time: 3.09s   best: 222.4015\n",
      "Epoch 1256/2000:  train Loss: 178.5056   val Loss: 224.7894   time: 3.08s   best: 222.4015\n",
      "Epoch 1257/2000:  train Loss: 178.3637   val Loss: 223.5526   time: 3.14s   best: 222.4015\n",
      "Epoch 1258/2000:  train Loss: 178.9684   val Loss: 224.9058   time: 3.17s   best: 222.4015\n",
      "Epoch 1259/2000:  train Loss: 178.9631   val Loss: 224.6707   time: 3.10s   best: 222.4015\n",
      "Epoch 1260/2000:  train Loss: 178.4256   val Loss: 224.2196   time: 3.10s   best: 222.4015\n",
      "Epoch 1261/2000:  train Loss: 178.8420   val Loss: 224.4692   time: 3.10s   best: 222.4015\n",
      "Epoch 1262/2000:  train Loss: 177.2075   val Loss: 225.5903   time: 3.09s   best: 222.4015\n",
      "Epoch 1263/2000:  train Loss: 178.4244   val Loss: 231.9210   time: 3.09s   best: 222.4015\n",
      "Epoch 1264/2000:  train Loss: 179.8000   val Loss: 227.5848   time: 3.09s   best: 222.4015\n",
      "Epoch 1265/2000:  train Loss: 177.9685   val Loss: 228.8619   time: 3.17s   best: 222.4015\n",
      "Epoch 1266/2000:  train Loss: 178.2670   val Loss: 224.2316   time: 3.15s   best: 222.4015\n",
      "Epoch 1267/2000:  train Loss: 181.1579   val Loss: 223.4620   time: 3.10s   best: 222.4015\n",
      "Epoch 1268/2000:  train Loss: 178.7672   val Loss: 225.2290   time: 3.10s   best: 222.4015\n",
      "Epoch 1269/2000:  train Loss: 177.7100   val Loss: 223.4189   time: 3.10s   best: 222.4015\n",
      "Epoch 1270/2000:  train Loss: 177.2482   val Loss: 226.5895   time: 3.09s   best: 222.4015\n",
      "Epoch 1271/2000:  train Loss: 179.5717   val Loss: 223.8218   time: 3.08s   best: 222.4015\n",
      "Epoch 1272/2000:  train Loss: 177.9716   val Loss: 224.5391   time: 3.10s   best: 222.4015\n",
      "Epoch 1273/2000:  train Loss: 179.1563   val Loss: 224.6843   time: 3.18s   best: 222.4015\n",
      "Epoch 1274/2000:  train Loss: 177.4602   val Loss: 222.9772   time: 3.14s   best: 222.4015\n",
      "Epoch 1275/2000:  train Loss: 178.2350   val Loss: 224.2188   time: 3.10s   best: 222.4015\n",
      "Epoch 1276/2000:  train Loss: 178.5058   val Loss: 225.7707   time: 3.10s   best: 222.4015\n",
      "Epoch 1277/2000:  train Loss: 179.2820   val Loss: 223.8648   time: 3.09s   best: 222.4015\n",
      "Epoch 1278/2000:  train Loss: 178.1387   val Loss: 222.9819   time: 3.09s   best: 222.4015\n",
      "Epoch 1279/2000:  train Loss: 178.7605   val Loss: 227.4821   time: 3.08s   best: 222.4015\n",
      "Epoch 1280/2000:  train Loss: 178.4004   val Loss: 225.1545   time: 3.10s   best: 222.4015\n",
      "Epoch 1281/2000:  train Loss: 178.7383   val Loss: 223.4346   time: 3.19s   best: 222.4015\n",
      "Epoch 1282/2000:  train Loss: 178.1202   val Loss: 229.6824   time: 3.13s   best: 222.4015\n",
      "Epoch 1283/2000:  train Loss: 178.4410   val Loss: 223.3693   time: 3.10s   best: 222.4015\n",
      "Epoch 1284/2000:  train Loss: 178.0949   val Loss: 224.9400   time: 3.10s   best: 222.4015\n",
      "Epoch 1285/2000:  train Loss: 178.1744   val Loss: 231.1997   time: 3.10s   best: 222.4015\n",
      "Epoch 1286/2000:  train Loss: 179.4650   val Loss: 224.0732   time: 3.09s   best: 222.4015\n",
      "Epoch 1287/2000:  train Loss: 178.0607   val Loss: 225.7947   time: 3.09s   best: 222.4015\n",
      "Epoch 1288/2000:  train Loss: 177.8777   val Loss: 224.8896   time: 3.13s   best: 222.4015\n",
      "Epoch 1289/2000:  train Loss: 178.2302   val Loss: 223.4303   time: 3.19s   best: 222.4015\n",
      "Epoch 1290/2000:  train Loss: 177.2857   val Loss: 224.5610   time: 3.10s   best: 222.4015\n",
      "Epoch 1291/2000:  train Loss: 177.1314   val Loss: 224.4774   time: 3.10s   best: 222.4015\n",
      "Epoch 1292/2000:  train Loss: 176.7049   val Loss: 227.0208   time: 3.10s   best: 222.4015\n",
      "Epoch 1293/2000:  train Loss: 179.2678   val Loss: 222.8993   time: 3.09s   best: 222.4015\n",
      "Epoch 1294/2000:  train Loss: 177.3583   val Loss: 225.4097   time: 3.09s   best: 222.4015\n",
      "Epoch 1295/2000:  train Loss: 177.4275   val Loss: 224.2224   time: 3.09s   best: 222.4015\n",
      "Epoch 1296/2000:  train Loss: 177.8844   val Loss: 225.1638   time: 3.17s   best: 222.4015\n",
      "Epoch 1297/2000:  train Loss: 178.1102   val Loss: 223.8672   time: 3.15s   best: 222.4015\n",
      "Epoch 1298/2000:  train Loss: 176.7094   val Loss: 226.1801   time: 3.09s   best: 222.4015\n",
      "Epoch 1299/2000:  train Loss: 180.8392   val Loss: 224.9266   time: 3.11s   best: 222.4015\n",
      "Epoch 1300/2000:  train Loss: 177.2056   val Loss: 226.9536   time: 3.10s   best: 222.4015\n",
      "Epoch 1301/2000:  train Loss: 176.9598   val Loss: 224.9928   time: 3.09s   best: 222.4015\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1302/2000:  train Loss: 176.3216   val Loss: 224.7890   time: 3.09s   best: 222.4015\n",
      "Epoch 1303/2000:  train Loss: 178.7154   val Loss: 226.6385   time: 3.10s   best: 222.4015\n",
      "Epoch 1304/2000:  train Loss: 177.2946   val Loss: 225.3674   time: 3.19s   best: 222.4015\n",
      "Epoch 1305/2000:  train Loss: 177.0303   val Loss: 226.4538   time: 3.13s   best: 222.4015\n",
      "Epoch 1306/2000:  train Loss: 176.9118   val Loss: 224.9510   time: 3.10s   best: 222.4015\n",
      "Epoch 1307/2000:  train Loss: 177.7157   val Loss: 227.3140   time: 3.10s   best: 222.4015\n",
      "Epoch 1308/2000:  train Loss: 176.9642   val Loss: 225.5166   time: 3.10s   best: 222.4015\n",
      "Epoch 1309/2000:  train Loss: 179.1232   val Loss: 226.5198   time: 3.10s   best: 222.4015\n",
      "Epoch 1310/2000:  train Loss: 176.1027   val Loss: 226.0912   time: 3.09s   best: 222.4015\n",
      "Epoch 1311/2000:  train Loss: 175.5865   val Loss: 227.1361   time: 3.13s   best: 222.4015\n",
      "Epoch 1312/2000:  train Loss: 176.5518   val Loss: 224.8416   time: 3.20s   best: 222.4015\n",
      "Epoch 1313/2000:  train Loss: 177.9143   val Loss: 224.6323   time: 3.11s   best: 222.4015\n",
      "Epoch 1314/2000:  train Loss: 176.4581   val Loss: 225.7909   time: 3.10s   best: 222.4015\n",
      "Epoch 1315/2000:  train Loss: 177.5954   val Loss: 226.6706   time: 3.10s   best: 222.4015\n",
      "Epoch 1316/2000:  train Loss: 177.0874   val Loss: 226.4925   time: 3.10s   best: 222.4015\n",
      "Epoch 1317/2000:  train Loss: 176.0183   val Loss: 227.0549   time: 3.09s   best: 222.4015\n",
      "Epoch 1318/2000:  train Loss: 175.9568   val Loss: 225.8620   time: 3.09s   best: 222.4015\n",
      "Epoch 1319/2000:  train Loss: 185.3763   val Loss: 233.8051   time: 3.19s   best: 222.4015\n",
      "Epoch 1320/2000:  train Loss: 178.9042   val Loss: 223.0991   time: 3.14s   best: 222.4015\n",
      "Epoch 1321/2000:  train Loss: 176.3614   val Loss: 224.9035   time: 3.10s   best: 222.4015\n",
      "Epoch 1322/2000:  train Loss: 175.3960   val Loss: 223.6578   time: 3.10s   best: 222.4015\n",
      "Epoch 1323/2000:  train Loss: 177.2647   val Loss: 223.0225   time: 3.10s   best: 222.4015\n",
      "Epoch 1324/2000:  train Loss: 177.5726   val Loss: 222.6357   time: 3.09s   best: 222.4015\n",
      "Epoch 1325/2000:  train Loss: 175.6952   val Loss: 224.7604   time: 3.09s   best: 222.4015\n",
      "Epoch 1326/2000:  train Loss: 176.9252   val Loss: 224.3225   time: 3.12s   best: 222.4015\n",
      "Epoch 1327/2000:  train Loss: 175.8220   val Loss: 223.8161   time: 3.19s   best: 222.4015\n",
      "Epoch 1328/2000:  train Loss: 177.2480   val Loss: 231.2805   time: 3.12s   best: 222.4015\n",
      "Epoch 1329/2000:  train Loss: 178.9678   val Loss: 226.9405   time: 3.10s   best: 222.4015\n",
      "Epoch 1330/2000:  train Loss: 175.4492   val Loss: 225.2135   time: 3.22s   best: 222.4015\n",
      "Epoch 1331/2000:  train Loss: 176.4070   val Loss: 224.4203   time: 3.22s   best: 222.4015\n",
      "Epoch 1332/2000:  train Loss: 175.4633   val Loss: 224.1184   time: 3.22s   best: 222.4015\n",
      "Epoch 1333/2000:  train Loss: 176.3605   val Loss: 225.4631   time: 3.22s   best: 222.4015\n",
      "Epoch 1334/2000:  train Loss: 175.5314   val Loss: 223.9622   time: 3.32s   best: 222.4015\n",
      "Epoch 1335/2000:  train Loss: 174.5049   val Loss: 226.7869   time: 3.29s   best: 222.4015\n",
      "Epoch 1336/2000:  train Loss: 176.4923   val Loss: 226.3542   time: 3.23s   best: 222.4015\n",
      "Epoch 1337/2000:  train Loss: 176.6498   val Loss: 225.7030   time: 3.23s   best: 222.4015\n",
      "Epoch 1338/2000:  train Loss: 175.9778   val Loss: 224.7887   time: 3.23s   best: 222.4015\n",
      "Epoch 1339/2000:  train Loss: 176.0221   val Loss: 222.1093   time: 3.22s   best: 222.1093\n",
      "Epoch 1340/2000:  train Loss: 174.7980   val Loss: 222.9659   time: 3.22s   best: 222.1093\n",
      "Epoch 1341/2000:  train Loss: 175.9736   val Loss: 224.0916   time: 3.26s   best: 222.1093\n",
      "Epoch 1342/2000:  train Loss: 175.4465   val Loss: 225.1483   time: 3.33s   best: 222.1093\n",
      "Epoch 1343/2000:  train Loss: 175.5733   val Loss: 226.5212   time: 3.25s   best: 222.1093\n",
      "Epoch 1344/2000:  train Loss: 175.8937   val Loss: 225.2234   time: 3.23s   best: 222.1093\n",
      "Epoch 1345/2000:  train Loss: 175.8176   val Loss: 226.0699   time: 3.22s   best: 222.1093\n",
      "Epoch 1346/2000:  train Loss: 175.9983   val Loss: 227.0152   time: 3.22s   best: 222.1093\n",
      "Epoch 1347/2000:  train Loss: 175.5388   val Loss: 227.8961   time: 3.22s   best: 222.1093\n",
      "Epoch 1348/2000:  train Loss: 176.7274   val Loss: 228.4755   time: 3.22s   best: 222.1093\n",
      "Epoch 1349/2000:  train Loss: 176.3052   val Loss: 223.2503   time: 3.33s   best: 222.1093\n",
      "Epoch 1350/2000:  train Loss: 177.4105   val Loss: 224.7918   time: 3.27s   best: 222.1093\n",
      "Epoch 1351/2000:  train Loss: 174.9262   val Loss: 222.7655   time: 3.23s   best: 222.1093\n",
      "Epoch 1352/2000:  train Loss: 176.5406   val Loss: 227.7201   time: 3.22s   best: 222.1093\n",
      "Epoch 1353/2000:  train Loss: 175.8325   val Loss: 223.5607   time: 3.15s   best: 222.1093\n",
      "Epoch 1354/2000:  train Loss: 174.8651   val Loss: 223.1484   time: 3.09s   best: 222.1093\n",
      "Epoch 1355/2000:  train Loss: 175.7137   val Loss: 222.4614   time: 3.08s   best: 222.1093\n",
      "Epoch 1356/2000:  train Loss: 175.2291   val Loss: 221.6583   time: 3.12s   best: 221.6583\n",
      "Epoch 1357/2000:  train Loss: 175.1042   val Loss: 222.4489   time: 3.19s   best: 221.6583\n",
      "Epoch 1358/2000:  train Loss: 175.9194   val Loss: 221.7632   time: 3.10s   best: 221.6583\n",
      "Epoch 1359/2000:  train Loss: 175.5517   val Loss: 224.0204   time: 3.10s   best: 221.6583\n",
      "Epoch 1360/2000:  train Loss: 174.9956   val Loss: 222.8362   time: 3.10s   best: 221.6583\n",
      "Epoch 1361/2000:  train Loss: 175.5600   val Loss: 223.5693   time: 3.10s   best: 221.6583\n",
      "Epoch 1362/2000:  train Loss: 176.0064   val Loss: 227.8027   time: 3.09s   best: 221.6583\n",
      "Epoch 1363/2000:  train Loss: 175.2960   val Loss: 222.1966   time: 3.09s   best: 221.6583\n",
      "Epoch 1364/2000:  train Loss: 174.7548   val Loss: 226.9723   time: 3.17s   best: 221.6583\n",
      "Epoch 1365/2000:  train Loss: 177.6817   val Loss: 222.1246   time: 3.16s   best: 221.6583\n",
      "Epoch 1366/2000:  train Loss: 174.9460   val Loss: 226.3388   time: 3.10s   best: 221.6583\n",
      "Epoch 1367/2000:  train Loss: 175.1940   val Loss: 220.8004   time: 3.10s   best: 220.8004\n",
      "Epoch 1368/2000:  train Loss: 174.5483   val Loss: 224.7369   time: 3.10s   best: 220.8004\n",
      "Epoch 1369/2000:  train Loss: 178.6539   val Loss: 222.7223   time: 3.09s   best: 220.8004\n",
      "Epoch 1370/2000:  train Loss: 174.7005   val Loss: 221.6871   time: 3.09s   best: 220.8004\n",
      "Epoch 1371/2000:  train Loss: 175.6393   val Loss: 219.6011   time: 3.12s   best: 219.6011\n",
      "Epoch 1372/2000:  train Loss: 174.6037   val Loss: 221.7679   time: 3.19s   best: 219.6011\n",
      "Epoch 1373/2000:  train Loss: 175.9217   val Loss: 222.5317   time: 3.13s   best: 219.6011\n",
      "Epoch 1374/2000:  train Loss: 174.6087   val Loss: 220.7754   time: 3.10s   best: 219.6011\n",
      "Epoch 1375/2000:  train Loss: 175.0529   val Loss: 221.2719   time: 3.10s   best: 219.6011\n",
      "Epoch 1376/2000:  train Loss: 174.2313   val Loss: 223.5214   time: 3.10s   best: 219.6011\n",
      "Epoch 1377/2000:  train Loss: 174.2124   val Loss: 222.0071   time: 3.09s   best: 219.6011\n",
      "Epoch 1378/2000:  train Loss: 174.7576   val Loss: 225.2970   time: 3.09s   best: 219.6011\n",
      "Epoch 1379/2000:  train Loss: 174.9569   val Loss: 222.3351   time: 3.16s   best: 219.6011\n",
      "Epoch 1380/2000:  train Loss: 174.1578   val Loss: 222.9611   time: 3.16s   best: 219.6011\n",
      "Epoch 1381/2000:  train Loss: 174.4778   val Loss: 222.4499   time: 3.10s   best: 219.6011\n",
      "Epoch 1382/2000:  train Loss: 173.8136   val Loss: 221.2073   time: 3.10s   best: 219.6011\n",
      "Epoch 1383/2000:  train Loss: 173.7598   val Loss: 222.2523   time: 3.10s   best: 219.6011\n",
      "Epoch 1384/2000:  train Loss: 174.3102   val Loss: 223.5827   time: 3.09s   best: 219.6011\n",
      "Epoch 1385/2000:  train Loss: 173.7529   val Loss: 228.0352   time: 3.09s   best: 219.6011\n",
      "Epoch 1386/2000:  train Loss: 175.0617   val Loss: 223.1684   time: 3.10s   best: 219.6011\n",
      "Epoch 1387/2000:  train Loss: 174.1478   val Loss: 225.3628   time: 3.19s   best: 219.6011\n",
      "Epoch 1388/2000:  train Loss: 175.9200   val Loss: 221.9309   time: 3.14s   best: 219.6011\n",
      "Epoch 1389/2000:  train Loss: 175.6475   val Loss: 222.1637   time: 3.10s   best: 219.6011\n",
      "Epoch 1390/2000:  train Loss: 173.5520   val Loss: 222.6695   time: 3.10s   best: 219.6011\n",
      "Epoch 1391/2000:  train Loss: 173.7860   val Loss: 222.0853   time: 3.09s   best: 219.6011\n",
      "Epoch 1392/2000:  train Loss: 174.5797   val Loss: 221.7088   time: 3.09s   best: 219.6011\n",
      "Epoch 1393/2000:  train Loss: 174.7340   val Loss: 225.0600   time: 3.08s   best: 219.6011\n",
      "Epoch 1394/2000:  train Loss: 175.2900   val Loss: 220.7534   time: 3.12s   best: 219.6011\n",
      "Epoch 1395/2000:  train Loss: 173.7662   val Loss: 222.4124   time: 3.19s   best: 219.6011\n",
      "Epoch 1396/2000:  train Loss: 174.5676   val Loss: 223.3070   time: 3.11s   best: 219.6011\n",
      "Epoch 1397/2000:  train Loss: 174.4973   val Loss: 222.7028   time: 3.10s   best: 219.6011\n",
      "Epoch 1398/2000:  train Loss: 175.3921   val Loss: 221.6797   time: 3.10s   best: 219.6011\n",
      "Epoch 1399/2000:  train Loss: 179.5516   val Loss: 223.6091   time: 3.09s   best: 219.6011\n",
      "Epoch 1400/2000:  train Loss: 174.1481   val Loss: 225.7625   time: 3.09s   best: 219.6011\n",
      "Epoch 1401/2000:  train Loss: 173.0298   val Loss: 222.0786   time: 3.09s   best: 219.6011\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1402/2000:  train Loss: 173.6524   val Loss: 227.9256   time: 3.17s   best: 219.6011\n",
      "Epoch 1403/2000:  train Loss: 173.7301   val Loss: 222.9147   time: 3.17s   best: 219.6011\n",
      "Epoch 1404/2000:  train Loss: 173.7488   val Loss: 223.0249   time: 3.11s   best: 219.6011\n",
      "Epoch 1405/2000:  train Loss: 173.2120   val Loss: 223.3824   time: 3.11s   best: 219.6011\n",
      "Epoch 1406/2000:  train Loss: 173.4334   val Loss: 222.6591   time: 3.11s   best: 219.6011\n",
      "Epoch 1407/2000:  train Loss: 173.8531   val Loss: 221.7688   time: 3.10s   best: 219.6011\n",
      "Epoch 1408/2000:  train Loss: 175.9709   val Loss: 223.9698   time: 3.08s   best: 219.6011\n",
      "Epoch 1409/2000:  train Loss: 173.6885   val Loss: 224.5595   time: 3.09s   best: 219.6011\n",
      "Epoch 1410/2000:  train Loss: 173.1053   val Loss: 223.1198   time: 3.20s   best: 219.6011\n",
      "Epoch 1411/2000:  train Loss: 174.7499   val Loss: 223.2725   time: 3.13s   best: 219.6011\n",
      "Epoch 1412/2000:  train Loss: 174.2447   val Loss: 224.6315   time: 3.10s   best: 219.6011\n",
      "Epoch 1413/2000:  train Loss: 172.9663   val Loss: 224.4139   time: 3.09s   best: 219.6011\n",
      "Epoch 1414/2000:  train Loss: 172.9706   val Loss: 223.1013   time: 3.11s   best: 219.6011\n",
      "Epoch 1415/2000:  train Loss: 173.5644   val Loss: 230.2195   time: 3.07s   best: 219.6011\n",
      "Epoch 1416/2000:  train Loss: 175.0374   val Loss: 225.8045   time: 3.08s   best: 219.6011\n",
      "Epoch 1417/2000:  train Loss: 173.0379   val Loss: 223.5402   time: 3.16s   best: 219.6011\n",
      "Epoch 1418/2000:  train Loss: 172.7573   val Loss: 223.8956   time: 3.18s   best: 219.6011\n",
      "Epoch 1419/2000:  train Loss: 172.9886   val Loss: 222.8757   time: 3.10s   best: 219.6011\n",
      "Epoch 1420/2000:  train Loss: 173.5488   val Loss: 225.6612   time: 3.10s   best: 219.6011\n",
      "Epoch 1421/2000:  train Loss: 174.0962   val Loss: 224.0856   time: 3.10s   best: 219.6011\n",
      "Epoch 1422/2000:  train Loss: 173.7457   val Loss: 224.4386   time: 3.09s   best: 219.6011\n",
      "Epoch 1423/2000:  train Loss: 172.2728   val Loss: 224.0155   time: 3.09s   best: 219.6011\n",
      "Epoch 1424/2000:  train Loss: 173.3678   val Loss: 225.3259   time: 3.11s   best: 219.6011\n",
      "Epoch 1425/2000:  train Loss: 172.6081   val Loss: 225.3306   time: 3.20s   best: 219.6011\n",
      "Epoch 1426/2000:  train Loss: 173.6267   val Loss: 223.1695   time: 3.12s   best: 219.6011\n",
      "Epoch 1427/2000:  train Loss: 172.9910   val Loss: 225.4505   time: 3.10s   best: 219.6011\n",
      "Epoch 1428/2000:  train Loss: 173.3295   val Loss: 229.2827   time: 3.10s   best: 219.6011\n",
      "Epoch 1429/2000:  train Loss: 173.5387   val Loss: 224.5717   time: 3.10s   best: 219.6011\n",
      "Epoch 1430/2000:  train Loss: 172.7573   val Loss: 225.7808   time: 3.09s   best: 219.6011\n",
      "Epoch 1431/2000:  train Loss: 173.6963   val Loss: 224.1430   time: 3.09s   best: 219.6011\n",
      "Epoch 1432/2000:  train Loss: 173.6726   val Loss: 222.0918   time: 3.15s   best: 219.6011\n",
      "Epoch 1433/2000:  train Loss: 173.4542   val Loss: 221.4929   time: 3.19s   best: 219.6011\n",
      "Epoch 1434/2000:  train Loss: 174.0540   val Loss: 224.2470   time: 3.11s   best: 219.6011\n",
      "Epoch 1435/2000:  train Loss: 172.6282   val Loss: 223.0271   time: 3.10s   best: 219.6011\n",
      "Epoch 1436/2000:  train Loss: 171.5625   val Loss: 223.8728   time: 3.10s   best: 219.6011\n",
      "Epoch 1437/2000:  train Loss: 173.0300   val Loss: 224.4835   time: 3.10s   best: 219.6011\n",
      "Epoch 1438/2000:  train Loss: 175.8004   val Loss: 239.6708   time: 3.09s   best: 219.6011\n",
      "Epoch 1439/2000:  train Loss: 177.5250   val Loss: 224.5493   time: 3.10s   best: 219.6011\n",
      "Epoch 1440/2000:  train Loss: 174.3188   val Loss: 222.0553   time: 3.19s   best: 219.6011\n",
      "Epoch 1441/2000:  train Loss: 173.8934   val Loss: 222.4936   time: 3.14s   best: 219.6011\n",
      "Epoch 1442/2000:  train Loss: 171.8460   val Loss: 224.0943   time: 3.10s   best: 219.6011\n",
      "Epoch 1443/2000:  train Loss: 172.7127   val Loss: 222.6507   time: 3.10s   best: 219.6011\n",
      "Epoch 1444/2000:  train Loss: 172.4418   val Loss: 224.5969   time: 3.10s   best: 219.6011\n",
      "Epoch 1445/2000:  train Loss: 171.9203   val Loss: 222.3864   time: 3.09s   best: 219.6011\n",
      "Epoch 1446/2000:  train Loss: 173.3694   val Loss: 222.8206   time: 3.09s   best: 219.6011\n",
      "Epoch 1447/2000:  train Loss: 172.0280   val Loss: 226.9257   time: 3.12s   best: 219.6011\n",
      "Epoch 1448/2000:  train Loss: 174.5393   val Loss: 221.4168   time: 3.20s   best: 219.6011\n",
      "Epoch 1449/2000:  train Loss: 172.8217   val Loss: 223.6086   time: 3.12s   best: 219.6011\n",
      "Epoch 1450/2000:  train Loss: 172.9658   val Loss: 219.8036   time: 3.10s   best: 219.6011\n",
      "Epoch 1451/2000:  train Loss: 172.3929   val Loss: 225.6278   time: 3.10s   best: 219.6011\n",
      "Epoch 1452/2000:  train Loss: 173.5816   val Loss: 224.1322   time: 3.10s   best: 219.6011\n",
      "Epoch 1453/2000:  train Loss: 172.8446   val Loss: 223.1295   time: 3.09s   best: 219.6011\n",
      "Epoch 1454/2000:  train Loss: 171.8455   val Loss: 225.5324   time: 3.09s   best: 219.6011\n",
      "Epoch 1455/2000:  train Loss: 171.6824   val Loss: 221.6821   time: 3.17s   best: 219.6011\n",
      "Epoch 1456/2000:  train Loss: 171.4347   val Loss: 221.3692   time: 3.17s   best: 219.6011\n",
      "Epoch 1457/2000:  train Loss: 172.1297   val Loss: 223.8985   time: 3.11s   best: 219.6011\n",
      "Epoch 1458/2000:  train Loss: 172.9216   val Loss: 223.1872   time: 3.11s   best: 219.6011\n",
      "Epoch 1459/2000:  train Loss: 170.8499   val Loss: 229.0651   time: 3.11s   best: 219.6011\n",
      "Epoch 1460/2000:  train Loss: 173.9282   val Loss: 231.4304   time: 3.09s   best: 219.6011\n",
      "Epoch 1461/2000:  train Loss: 174.5047   val Loss: 222.4218   time: 3.09s   best: 219.6011\n",
      "Epoch 1462/2000:  train Loss: 171.4761   val Loss: 221.5370   time: 3.11s   best: 219.6011\n",
      "Epoch 1463/2000:  train Loss: 171.6724   val Loss: 222.6417   time: 3.21s   best: 219.6011\n",
      "Epoch 1464/2000:  train Loss: 171.6389   val Loss: 222.4052   time: 3.11s   best: 219.6011\n",
      "Epoch 1465/2000:  train Loss: 170.7253   val Loss: 223.5199   time: 3.10s   best: 219.6011\n",
      "Epoch 1466/2000:  train Loss: 172.2143   val Loss: 223.3488   time: 3.10s   best: 219.6011\n",
      "Epoch 1467/2000:  train Loss: 172.4083   val Loss: 229.1635   time: 3.09s   best: 219.6011\n",
      "Epoch 1468/2000:  train Loss: 172.5547   val Loss: 226.7767   time: 3.09s   best: 219.6011\n",
      "Epoch 1469/2000:  train Loss: 171.8514   val Loss: 222.4709   time: 3.08s   best: 219.6011\n",
      "Epoch 1470/2000:  train Loss: 171.6793   val Loss: 222.7133   time: 3.15s   best: 219.6011\n",
      "Epoch 1471/2000:  train Loss: 173.1773   val Loss: 239.5998   time: 3.19s   best: 219.6011\n",
      "Epoch 1472/2000:  train Loss: 173.4694   val Loss: 221.6791   time: 3.11s   best: 219.6011\n",
      "Epoch 1473/2000:  train Loss: 171.8806   val Loss: 230.2939   time: 3.46s   best: 219.6011\n",
      "Epoch 1474/2000:  train Loss: 173.1429   val Loss: 221.9479   time: 3.48s   best: 219.6011\n",
      "Epoch 1475/2000:  train Loss: 171.5282   val Loss: 221.5025   time: 3.23s   best: 219.6011\n",
      "Epoch 1476/2000:  train Loss: 172.4153   val Loss: 224.1488   time: 3.22s   best: 219.6011\n",
      "Epoch 1477/2000:  train Loss: 176.6529   val Loss: 278.5580   time: 3.24s   best: 219.6011\n",
      "Epoch 1478/2000:  train Loss: 187.6794   val Loss: 228.1733   time: 3.35s   best: 219.6011\n",
      "Epoch 1479/2000:  train Loss: 175.0796   val Loss: 222.6688   time: 3.25s   best: 219.6011\n",
      "Epoch 1480/2000:  train Loss: 172.2784   val Loss: 222.2814   time: 3.23s   best: 219.6011\n",
      "Epoch 1481/2000:  train Loss: 171.2945   val Loss: 223.0017   time: 3.23s   best: 219.6011\n",
      "Epoch 1482/2000:  train Loss: 171.1637   val Loss: 221.4475   time: 3.22s   best: 219.6011\n",
      "Epoch 1483/2000:  train Loss: 172.0841   val Loss: 224.0137   time: 3.22s   best: 219.6011\n",
      "Epoch 1484/2000:  train Loss: 170.6407   val Loss: 224.1368   time: 3.22s   best: 219.6011\n",
      "Epoch 1485/2000:  train Loss: 170.4226   val Loss: 222.1464   time: 3.31s   best: 219.6011\n",
      "Epoch 1486/2000:  train Loss: 170.3143   val Loss: 223.2784   time: 3.31s   best: 219.6011\n",
      "Epoch 1487/2000:  train Loss: 171.4851   val Loss: 222.7727   time: 3.23s   best: 219.6011\n",
      "Epoch 1488/2000:  train Loss: 174.4984   val Loss: 230.0192   time: 3.23s   best: 219.6011\n",
      "Epoch 1489/2000:  train Loss: 174.3979   val Loss: 222.8794   time: 3.23s   best: 219.6011\n",
      "Epoch 1490/2000:  train Loss: 172.5845   val Loss: 222.9796   time: 3.23s   best: 219.6011\n",
      "Epoch 1491/2000:  train Loss: 171.1300   val Loss: 223.6142   time: 3.22s   best: 219.6011\n",
      "Epoch 1492/2000:  train Loss: 172.6655   val Loss: 220.5344   time: 3.27s   best: 219.6011\n",
      "Epoch 1493/2000:  train Loss: 171.6254   val Loss: 221.3953   time: 3.35s   best: 219.6011\n",
      "Epoch 1494/2000:  train Loss: 170.7760   val Loss: 221.9482   time: 3.24s   best: 219.6011\n",
      "Epoch 1495/2000:  train Loss: 170.7149   val Loss: 223.9612   time: 3.23s   best: 219.6011\n",
      "Epoch 1496/2000:  train Loss: 170.9078   val Loss: 221.4420   time: 3.22s   best: 219.6011\n",
      "Epoch 1497/2000:  train Loss: 171.1982   val Loss: 223.3926   time: 3.10s   best: 219.6011\n",
      "Epoch 1498/2000:  train Loss: 170.4128   val Loss: 221.8541   time: 3.09s   best: 219.6011\n",
      "Epoch 1499/2000:  train Loss: 171.1108   val Loss: 222.3244   time: 3.10s   best: 219.6011\n",
      "Epoch 1500/2000:  train Loss: 170.9318   val Loss: 222.7563   time: 3.70s   best: 219.6011\n",
      "Epoch 1501/2000:  train Loss: 172.9014   val Loss: 224.2411   time: 3.59s   best: 219.6011\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1502/2000:  train Loss: 170.2837   val Loss: 221.7290   time: 3.18s   best: 219.6011\n",
      "Epoch 1503/2000:  train Loss: 170.9165   val Loss: 223.1100   time: 3.10s   best: 219.6011\n",
      "Epoch 1504/2000:  train Loss: 170.8932   val Loss: 222.7907   time: 3.10s   best: 219.6011\n",
      "Epoch 1505/2000:  train Loss: 171.0796   val Loss: 223.3777   time: 3.09s   best: 219.6011\n",
      "Epoch 1506/2000:  train Loss: 170.6764   val Loss: 223.5593   time: 3.09s   best: 219.6011\n",
      "Epoch 1507/2000:  train Loss: 169.7430   val Loss: 220.8465   time: 3.14s   best: 219.6011\n",
      "Epoch 1508/2000:  train Loss: 171.2604   val Loss: 221.4963   time: 3.18s   best: 219.6011\n",
      "Epoch 1509/2000:  train Loss: 169.8767   val Loss: 222.8414   time: 3.10s   best: 219.6011\n",
      "Epoch 1510/2000:  train Loss: 172.3995   val Loss: 220.9779   time: 3.10s   best: 219.6011\n",
      "Epoch 1511/2000:  train Loss: 173.7403   val Loss: 225.2739   time: 3.10s   best: 219.6011\n",
      "Epoch 1512/2000:  train Loss: 170.3439   val Loss: 221.5181   time: 3.09s   best: 219.6011\n",
      "Epoch 1513/2000:  train Loss: 170.5547   val Loss: 221.5883   time: 3.09s   best: 219.6011\n",
      "Epoch 1514/2000:  train Loss: 172.7974   val Loss: 222.2343   time: 3.09s   best: 219.6011\n",
      "Epoch 1515/2000:  train Loss: 175.3245   val Loss: 234.0123   time: 3.16s   best: 219.6011\n",
      "Epoch 1516/2000:  train Loss: 177.3908   val Loss: 221.6905   time: 3.17s   best: 219.6011\n",
      "Epoch 1517/2000:  train Loss: 171.5468   val Loss: 222.6360   time: 3.10s   best: 219.6011\n",
      "Epoch 1518/2000:  train Loss: 170.3270   val Loss: 219.6884   time: 3.09s   best: 219.6011\n",
      "Epoch 1519/2000:  train Loss: 169.7035   val Loss: 222.0492   time: 3.10s   best: 219.6011\n",
      "Epoch 1520/2000:  train Loss: 170.6111   val Loss: 224.0085   time: 3.08s   best: 219.6011\n",
      "Epoch 1521/2000:  train Loss: 169.9257   val Loss: 223.4627   time: 3.08s   best: 219.6011\n",
      "Epoch 1522/2000:  train Loss: 169.8366   val Loss: 221.6925   time: 3.09s   best: 219.6011\n",
      "Epoch 1523/2000:  train Loss: 170.2419   val Loss: 223.9702   time: 3.18s   best: 219.6011\n",
      "Epoch 1524/2000:  train Loss: 171.0360   val Loss: 224.9954   time: 3.14s   best: 219.6011\n",
      "Epoch 1525/2000:  train Loss: 170.1902   val Loss: 222.5602   time: 3.10s   best: 219.6011\n",
      "Epoch 1526/2000:  train Loss: 171.3076   val Loss: 221.6983   time: 3.10s   best: 219.6011\n",
      "Epoch 1527/2000:  train Loss: 170.0902   val Loss: 222.0331   time: 3.10s   best: 219.6011\n",
      "Epoch 1528/2000:  train Loss: 169.1886   val Loss: 224.4198   time: 3.09s   best: 219.6011\n",
      "Epoch 1529/2000:  train Loss: 171.7514   val Loss: 223.5539   time: 3.09s   best: 219.6011\n",
      "Epoch 1530/2000:  train Loss: 170.9549   val Loss: 222.3149   time: 3.12s   best: 219.6011\n",
      "Epoch 1531/2000:  train Loss: 169.5434   val Loss: 226.6075   time: 3.20s   best: 219.6011\n",
      "Epoch 1532/2000:  train Loss: 170.2363   val Loss: 220.9789   time: 3.11s   best: 219.6011\n",
      "Epoch 1533/2000:  train Loss: 169.7694   val Loss: 221.8053   time: 3.10s   best: 219.6011\n",
      "Epoch 1534/2000:  train Loss: 170.5338   val Loss: 223.9340   time: 3.10s   best: 219.6011\n",
      "Epoch 1535/2000:  train Loss: 170.9429   val Loss: 222.9935   time: 3.09s   best: 219.6011\n",
      "Epoch 1536/2000:  train Loss: 169.7994   val Loss: 222.2035   time: 3.09s   best: 219.6011\n",
      "Epoch 1537/2000:  train Loss: 169.8164   val Loss: 222.5482   time: 3.10s   best: 219.6011\n",
      "Epoch 1538/2000:  train Loss: 168.8275   val Loss: 223.2978   time: 3.19s   best: 219.6011\n",
      "Epoch 1539/2000:  train Loss: 170.7251   val Loss: 230.5642   time: 3.14s   best: 219.6011\n",
      "Epoch 1540/2000:  train Loss: 172.1458   val Loss: 221.1622   time: 3.10s   best: 219.6011\n",
      "Epoch 1541/2000:  train Loss: 169.1083   val Loss: 224.9216   time: 3.10s   best: 219.6011\n",
      "Epoch 1542/2000:  train Loss: 169.4998   val Loss: 222.7632   time: 3.10s   best: 219.6011\n",
      "Epoch 1543/2000:  train Loss: 170.8162   val Loss: 225.4985   time: 3.09s   best: 219.6011\n",
      "Epoch 1544/2000:  train Loss: 172.9760   val Loss: 227.1624   time: 3.09s   best: 219.6011\n",
      "Epoch 1545/2000:  train Loss: 170.8488   val Loss: 225.7140   time: 3.12s   best: 219.6011\n",
      "Epoch 1546/2000:  train Loss: 169.4004   val Loss: 225.3735   time: 3.19s   best: 219.6011\n",
      "Epoch 1547/2000:  train Loss: 169.2226   val Loss: 222.0474   time: 3.12s   best: 219.6011\n",
      "Epoch 1548/2000:  train Loss: 170.4474   val Loss: 223.4769   time: 3.10s   best: 219.6011\n",
      "Epoch 1549/2000:  train Loss: 171.6817   val Loss: 220.9468   time: 3.10s   best: 219.6011\n",
      "Epoch 1550/2000:  train Loss: 170.5459   val Loss: 222.6195   time: 3.09s   best: 219.6011\n",
      "Epoch 1551/2000:  train Loss: 169.3560   val Loss: 222.3940   time: 3.09s   best: 219.6011\n",
      "Epoch 1552/2000:  train Loss: 169.9761   val Loss: 222.6766   time: 3.09s   best: 219.6011\n",
      "Epoch 1553/2000:  train Loss: 169.3746   val Loss: 224.0356   time: 3.15s   best: 219.6011\n",
      "Epoch 1554/2000:  train Loss: 169.7126   val Loss: 221.6324   time: 3.18s   best: 219.6011\n",
      "Epoch 1555/2000:  train Loss: 169.8484   val Loss: 222.4227   time: 3.10s   best: 219.6011\n",
      "Epoch 1556/2000:  train Loss: 170.5641   val Loss: 222.0727   time: 3.10s   best: 219.6011\n",
      "Epoch 1557/2000:  train Loss: 169.6152   val Loss: 221.4436   time: 3.10s   best: 219.6011\n",
      "Epoch 1558/2000:  train Loss: 168.8275   val Loss: 224.0563   time: 3.09s   best: 219.6011\n",
      "Epoch 1559/2000:  train Loss: 169.3811   val Loss: 225.4182   time: 3.09s   best: 219.6011\n",
      "Epoch 1560/2000:  train Loss: 170.5544   val Loss: 223.4216   time: 3.10s   best: 219.6011\n",
      "Epoch 1561/2000:  train Loss: 169.1865   val Loss: 224.7925   time: 3.19s   best: 219.6011\n",
      "Epoch 1562/2000:  train Loss: 169.3453   val Loss: 220.5183   time: 3.13s   best: 219.6011\n",
      "Epoch 1563/2000:  train Loss: 169.7834   val Loss: 222.6713   time: 3.10s   best: 219.6011\n",
      "Epoch 1564/2000:  train Loss: 170.0710   val Loss: 224.9498   time: 3.10s   best: 219.6011\n",
      "Epoch 1565/2000:  train Loss: 171.8910   val Loss: 221.2840   time: 3.10s   best: 219.6011\n",
      "Epoch 1566/2000:  train Loss: 168.8408   val Loss: 221.7231   time: 3.09s   best: 219.6011\n",
      "Epoch 1567/2000:  train Loss: 169.4371   val Loss: 219.4391   time: 3.09s   best: 219.4391\n",
      "Epoch 1568/2000:  train Loss: 170.8625   val Loss: 220.6937   time: 3.12s   best: 219.4391\n",
      "Epoch 1569/2000:  train Loss: 170.5032   val Loss: 221.6760   time: 3.20s   best: 219.4391\n",
      "Epoch 1570/2000:  train Loss: 168.6105   val Loss: 221.8226   time: 3.11s   best: 219.4391\n",
      "Epoch 1571/2000:  train Loss: 169.4579   val Loss: 222.9643   time: 3.10s   best: 219.4391\n",
      "Epoch 1572/2000:  train Loss: 168.5474   val Loss: 224.4376   time: 3.10s   best: 219.4391\n",
      "Epoch 1573/2000:  train Loss: 169.1216   val Loss: 220.1480   time: 3.09s   best: 219.4391\n",
      "Epoch 1574/2000:  train Loss: 168.6752   val Loss: 222.7924   time: 3.08s   best: 219.4391\n",
      "Epoch 1575/2000:  train Loss: 168.8658   val Loss: 223.0016   time: 3.09s   best: 219.4391\n",
      "Epoch 1576/2000:  train Loss: 169.2563   val Loss: 223.6547   time: 3.16s   best: 219.4391\n",
      "Epoch 1577/2000:  train Loss: 170.0048   val Loss: 221.8586   time: 3.15s   best: 219.4391\n",
      "Epoch 1578/2000:  train Loss: 168.5814   val Loss: 222.5871   time: 3.09s   best: 219.4391\n",
      "Epoch 1579/2000:  train Loss: 168.2518   val Loss: 224.5382   time: 3.09s   best: 219.4391\n",
      "Epoch 1580/2000:  train Loss: 170.3894   val Loss: 224.8116   time: 3.09s   best: 219.4391\n",
      "Epoch 1581/2000:  train Loss: 168.5582   val Loss: 222.3436   time: 3.09s   best: 219.4391\n",
      "Epoch 1582/2000:  train Loss: 169.6753   val Loss: 219.6857   time: 3.09s   best: 219.4391\n",
      "Epoch 1583/2000:  train Loss: 170.6471   val Loss: 220.4835   time: 3.10s   best: 219.4391\n",
      "Epoch 1584/2000:  train Loss: 168.0048   val Loss: 220.1652   time: 3.20s   best: 219.4391\n",
      "Epoch 1585/2000:  train Loss: 167.9312   val Loss: 222.5226   time: 3.12s   best: 219.4391\n",
      "Epoch 1586/2000:  train Loss: 169.2539   val Loss: 221.5388   time: 3.10s   best: 219.4391\n",
      "Epoch 1587/2000:  train Loss: 169.2338   val Loss: 224.2944   time: 3.10s   best: 219.4391\n",
      "Epoch 1588/2000:  train Loss: 168.9265   val Loss: 223.4798   time: 3.10s   best: 219.4391\n",
      "Epoch 1589/2000:  train Loss: 169.1410   val Loss: 232.4449   time: 3.09s   best: 219.4391\n",
      "Epoch 1590/2000:  train Loss: 171.0556   val Loss: 221.9641   time: 3.09s   best: 219.4391\n",
      "Epoch 1591/2000:  train Loss: 168.4273   val Loss: 222.6377   time: 3.14s   best: 219.4391\n",
      "Epoch 1592/2000:  train Loss: 170.7214   val Loss: 220.0755   time: 3.18s   best: 219.4391\n",
      "Epoch 1593/2000:  train Loss: 168.4376   val Loss: 221.0794   time: 3.10s   best: 219.4391\n",
      "Epoch 1594/2000:  train Loss: 168.0580   val Loss: 220.2822   time: 3.10s   best: 219.4391\n",
      "Epoch 1595/2000:  train Loss: 167.9289   val Loss: 221.5361   time: 3.10s   best: 219.4391\n",
      "Epoch 1596/2000:  train Loss: 168.4701   val Loss: 221.8597   time: 3.09s   best: 219.4391\n",
      "Epoch 1597/2000:  train Loss: 168.2258   val Loss: 222.8136   time: 3.09s   best: 219.4391\n",
      "Epoch 1598/2000:  train Loss: 168.2655   val Loss: 222.8546   time: 3.09s   best: 219.4391\n",
      "Epoch 1599/2000:  train Loss: 168.4886   val Loss: 222.1707   time: 3.19s   best: 219.4391\n",
      "Epoch 1600/2000:  train Loss: 168.0327   val Loss: 220.6469   time: 3.14s   best: 219.4391\n",
      "Epoch 1601/2000:  train Loss: 168.8747   val Loss: 222.6038   time: 3.10s   best: 219.4391\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hj+bmm28mMzOzznPOOecc4uPj6dGjB7169WLnzp21jpk8eTL9+/cnEAgwfvx4Nm7cyKpVqxg4cGDVc89NCdpffPEF6enp9OzZk5iYGC6//HLmzp3L4MGDWb9+PTfeeCPvvPMOXbp0AWDs2LFcfvnlPP/88/U2+4uIyMHn+7/ATakRZ+fk0L1kE3Q7AhK6tHpeEhMTq7Z//etfM3XqVF577TU2btxY78jC+Pj4qu1gMEhFRUWjjrEtaOKv79zU1FSWLVvGnDlzeOyxx5g5cyb//Oc/eeutt5g7dy6zZs3i3nvvJTMzU8FbRKQNRFdNu2oyloO/PGdeXh79+rm5Y5555plWv/6IESPYuHEjGzduBOCll15q9LnHHHMMH330EXv27CEUCvHiiy9y8skns2fPHsLhMBdeeCH33nsvixcvJhwOs2XLFqZOncof/vAHcnNzm/WMvIiItFxUVZda65Gvxrj11lu58sorefjhhznllFNa/fqdOnXi4Ycf5swzz6RHjx5Mnjy53mPff/99+vfvX/X55Zdf5v7772fq1KlYazn77LOZNm0ay5Yt46qrriLsLV16//33EwqFuOKKK8jLy8Nay80330zXrl1bvTwiItII1tp2/TrqqKPs/lasWFErrTF25+Rau3WxtUU5zTq/vdm2bZu11tpwOGxvuOEG+/DDD7dxjhpW3+/tww8/PLQZOYhUlvapo5Slo5TDWpWlIcBCW09MjK7m8UqHoKZ9KDzzzDOMHz+eUaNGkZeXx3XXXdfWWRIRkYMoqprHK/u0LXVPhO43P/7xj7njjjvaOhsiInKIRFdN2xy6Pm0REZHWFlVB21BZ01bQFhER/4mqoF3VJq6atoiI+FBUBe2qmraCtoiI+FBUBe3WmlwlPT2dOXPm1Ej785//zA9/+MMGz1m4cCEAZ599dp1zeN9999089NBDDX7366+/zooVK6o+/+Y3v+G9995rQu7rpiU8RUTav+gM2i2saF922WXMmDGjRtqMGTMaPf/322+/3ewJSvYP2vfccw+nnXZas64lIiL+ElVBu7UGol100UW8+eablJaWArBx40a2bdvGiSeeyA033MCkSZMYNWoUd911V53nDxw4kD179gBw3333MXz4cE477bSq5TsB/vGPf3D00Uczbtw4LrzwQoqKipg3bx6zZs3iF7/4BePHj2f9+vVMnz6dV155BXAzn02YMIExY8Zw9dVXV+Vv4MCB3HXXXUycOJExY8awatWqRpdVS3iKiLQf/n9Oe/btsOOrRh2aHA5DRSHBYBwE4+s/sPcYOOuBend3796dyZMn88477zBt2jRmzJjBJZdcgjGG++67j27duhEKhTj11FP58ssvGTt2bJ3XWbRoETNmzGDJkiVUVFQwceJEjjrqKAAuuOACfvCDHwDwq1/9iqeeeoobb7yR8847j3PPPZeLLrqI/Pz8qmuVlJQwffp03n//fYYNG8b3vvc9Hn/8cX76058C0KNHDxYvXsxf//pXHnroIZ588skD/ry0hKeISPsSVTXtKq0wDi2yiTyyaXzmzJlMnDiRCRMmkJmZWaMpe38ff/wx3/rWt+jcuTNdunThvPPOq9q3fPlypkyZwpgxY3jhhRfqXdqz0urVqxk0aBDDhg0D4Morr2Tu3LlV+y+44AIAjjrqqKpFRg5ES3iKiLQv/v/L2kCNeH+FRWWk5GZS0bkXsV37tehrzz//fG655RYWL15McXExEydOZMOGDTz00EN88cUXpKamMn36dEpKShq8jjF1z802ffp0Xn/9dcaNG8czzzxDRkZGg9c50Ij4yuU961v+synX1BKeIiJtI6pq2sYYwpZWqWknJSWRnp7O1VdfXVXL3rdvH4mJiaSkpLBz505mz57d4DVOOukkXnvtNYqLi8nPz+e///1v1b78/Hz69OlDeXk5L7zwQlV6cnJyjWbxSpVLda5duxaA5557jpNPPrlFZdQSniIi7Ut0VYNM5X9a5zntyy67jAsuuKCqmXzcuHFMmDCBUaNGMXjwYE444YQGz584cSKXXHIJ48eP5/DDD2fKlClV++69916OOeYYDj/8cMaMGVMVqC+99FJ+8IMf8Oijj9ZYpzshIYGnn36aiy++mIqKCo4++miuv/76JpVHS3iKiLRvpr1PNDJp0iRb+XxzpZUrV3LkkUc2+Vr5JeV0zs4k3Kk7sd0GtFYW20x+fj7JycltnY1Gq+/3lpGRQXp6+qHP0EGgsrRPHaUsHaUcoLI0xBizyFo7qa590dU8jgGM5h4XERFfiq6gbbyGccVsERHxId8G7eY269tW7NOWxmvv3TAiIn7gy6CdkJBAdnZ2kwOB0SpfbcJaS3Z2NgkJCW2dFRERX/Pl6PH+/fuTlZXF7t27m3ReeSiMyd8FMXnE7C4+SLk7dEpKSnwTCBMSEmqMTBcRkabzZdCOjY1l0KBBTT7v6535xM08n+Dhx9D/mucPQs4OrYyMDCZMmNDW2RARkUPEl83jzRUMGEIEIBxq66yIiIg0WVQF7dhggBBBCDduGk8REZH2JKqCdjBgqFBNW0REfCqqgnZMwLiatlVNW0RE/Ce6gnYwoD5tERHxragK2pUD0Yz6tEVExIeiKmjHBg0VBMGqpi0iIv4TVUE7GDBU2CAmVN7WWREREWmyqArasYEAxcQTEy5p66yIiIg0WVQF7UDAUEIcwZCCtoiI+E9UBW2AUhNPTMj/846LiEj0ibqgXWbiiVFNW0REfCjqgnapiScurJq2iIj4T9QF7TITT6wt0wQrIiLiO1EXtCsC3vrT5apti4iIv0Rd0C438d5GUdtmREREpIlaFLSNMV2NMa8YY1YZY1YaY44zxnQzxrxrjPnae0+NOP4OY8xaY8xqY8wZLc9+01UE4tyGgraIiPhMS2vajwDvWGtHAOOAlcDtwPvW2qHA+95njDEjgUuBUcCZwF+NMcEWfn+TlVc2j5cpaIuIiL80O2gbY7oAJwFPAVhry6y1ucA04FnvsGeB873tacAMa22ptXYDsBaY3Nzvb65wsLJ5XH3aIiLiLzEtOHcwsBt42hgzDlgE/ARIs9ZuB7DWbjfG9PKO7wfMjzg/y0urxRhzLXAtQFpaGhkZGS3IZk1l1hV56Refkrs2v9Wu2xYKCgpa9WfTVjpKOUBlaa86Slk6SjlAZWmulgTtGGAicKO19nNjzCN4TeH1MHWk2boOtNY+ATwBMGnSJJuent6CbNY0d2kmFMH4UcNhWOtdty1kZGTQmj+bttJRygEqS3vVUcrSUcoBKktztaRPOwvIstZ+7n1+BRfEdxpj+gB477sijh8QcX5/YFsLvr9ZqprHywoP9VeLiIi0SLODtrV2B7DFGDPcSzoVWAHMAq700q4E3vC2ZwGXGmPijTGDgKHAguZ+f3OFg24gmlXQFhERn2lJ8zjAjcALxpg4YD1wFe5GYKYx5hpgM3AxgLU20xgzExfYK4AfWWsP/bRkMa6mHSoranHhRUREDqUWxS1r7VJgUh27Tq3n+PuA+1rynS1lvZp2RUmBgraIiPhK1M2IFohxk6uEStQ8LiIi/hJ1QTsmGGCvTSJcuKetsyIiItIkURe044OGHTYVk7+9rbMiIiLSJFEXtGODsNt2JVC468AHi4iItCNRF7TjAoY8EjGleW2dFRERkSaJuqAdHwN5NpGggraIiPhM1AXtpFhX044p2we2zllURURE2qWoC9qdYw05tgsBWwFFOW2dHRERkUaLuqCdGAMbbZr7sHdD22ZGRESkCaIuaAcDht2x3oqgOevbNjMiIiJNEHVBGyA/oR9hjIK2iIj4SlQG7U6dE9kb7KmgLSIivhKVQbtr51i2BPvDug8gVNHW2REREWmUqAzaKZ1i+dyMg8LdUKQ5yEVExB+iMmh37RzLuoru7sP2ZW2bGRERkUaKyqDdpVMsWaWJ7sO/v922mREREWmkqAzaqZ3jmF8xtDpBk6yIiIgPRGXQTusSTziy6Lmb2y4zIiIijRSdQTs5AYAdw65wCU+c3Ia5ERERaZyoDNq9urigvTllUhvnREREpPGiMmindYkH4KvOx7RxTkRERBovKoN2UnwMyfExbNoXhlN/4xK3fNG2mRIRETmAqAzaxhgG9Uxkw55CGHGuS9z0SdtmSkRE5ACiMmgDDO6RyPrdhdBzOHTpD6tnw90psPw/bZ01ERGROkVt0B7UI4ltecWUlIeg73jY8rnb8eXLbZovERGR+kRt0B7cMxFrqdlEDoCtffDX78GWBTXTPvidq5mHQwc1nyIiIpWiOmiDF7R7DKu5c982KMmr/vzChfDU6dWfFz4Ncx9026Hy6nOy1x3EHIuISLSLaesMtJVBPVzQXr0jn7MHH15z58NHQtoYuKGewWlv3ly9bUPV51Qa9S24+JnWy6yIiAhRXNPuHBfDuAFdeXfFTujcvXpH9lr3vvOr+k8OBKu362oez3ytdTIpIiISIWqDNsCkw1NZv6eAsAXiklxiZdDuPhQ2zYPy4tonmogfW7jioOdTREQEojxoH9mnCyXlYT5euwd+/nXNncV74emz4L7etU80ETXt1bMhd0vDX1SSBznrW55hERGJalEdtM8YlUYwYHhj6VaI6wyn/Kp6Z2l+7RM2zXPvkc3jb/wQ/j6l4S96Yio8OqHlGRYRkagW1UE7OSGW4wZ3Z9V2L0BP+G71zlBp7ROePgvWzKnZPA6uVt6QHI0qFxGRlovqoA1w4tAerNi+j83ZRZBcR1P4/vKywJiDnzEREZH9RH3Q/ua4vgDMWrbVJZx6V8MnBGJqPsNdH/Vhi4hIK4v6oN2vayeOHpjKy4uyCIUtTLkFBp1c/wnLX2nchYtyWieDIiIinqgP2gDfOeYwNmUX8WVWrku4/GU4/291H7xhbuMuauuYDlVERKQFFLSBk4b2BGDeumyXEBMP4y9r2UVtuHZauI40ERGRRlLQBronxTOidzLvrdyJjawh9x7T/IuGy2unWS0uIiIizaeg7fnmuL4s2ZzL8/M3VSde8y6kDIDkvk2/YEVJ7TStCCYiIi2goO05Y1QaAL9+I7M6MbYT3Lwcbvi06Rcsrytoa8pTERFpPgVtz5BeyVWBe29hWc2dnVKbfsFXroIvX66ZpqAtIiIt0OKgbYwJGmOWGGPe9D53M8a8a4z52ntPjTj2DmPMWmPMamPMGS397tb2w/QhANw1K7PmDmPgF+vgpw2s/LW/UBm8+n3Y9Fl1Wl2D00RERBqpNWraPwFWRny+HXjfWjsUeN/7jDFmJHApMAo4E/irMZErb7S9Mf1SAJi1bBsFpfvVihN7QNfDYMrPYOqd8P0P4Ojvw4m3NHzRd39dva2atoiItECLgrYxpj9wDvBkRPI04Flv+1ng/Ij0GdbaUmvtBmAtMLkl39/aAgHDny4ZB8CV/1xQ90Gn/gZOvhX6HwXn/BFOO8AMagW7qrcjB6KFymHBPyCkQC4iIo1jbAsmATHGvALcDyQDP7fWnmuMybXWdo04Zq+1NtUY8xdgvrX2eS/9KWC2tbbWFGPGmGuBawHS0tKOmjFjRrPzuL+CggKSkpLq3b+rKMytc90a2lccGccph8UQOMBc413yVlMR04nJX9wIQGlcKvFlbhGRws4DSCxyS3d+duyTlCa4Z8L7b5nFkHVP8fWQ77O1/zcPSln8oqOUA1SW9qqjlKWjlANUloZMnTp1kbV2Ul37Ypp7UWPMucAua+0iY0x6Y06pI63OOwZr7RPAEwCTJk2y6emNuXzjZGRkcKDrfZS7mLe+3M7zK8s44/jxTPEmX6mfd70TpkJJLvExCfAX9/OuDNgAxx1zNKQOdB8++BTWwdD+vRja2PJtng9bPocTftLosvhBRykHqCztVUcpS0cpB6gszdXsoA2cAJxnjDkbSAC6GGOeB3YaY/pYa7cbY/oAle3DWcCAiPP7A9ta8P0HzaVHD+CtL7cDsKegjiU669N1ADCg/uexW/qc9j+9sXte0BYRkejS7D5ta+0d1tr+1tqBuAFmH1hrrwBmAVd6h10JvOFtzwIuNcbEG2MGAUOBejqO29aUoT35YfoRAHxWObVpUwSCru97f+EQfPoIPDi08dfau0mLj4iICNCymnZ9HgBmGmOuATYDFwNYazONMTOBFUAF8CNr2++8nreeOYLMbfuYuTCLYwd354KJ/Zt2gSk/g70bYfG/qtP+fbFLA+rpGajtkbGQkAK3b27a94uISIfTKpOrWGszrLXnetvZ1tpTrbVDvfeciOPus9YeYa0dbq2d3RrffTCdO7YPALfMXMZlT8xv+gXOfggumwHnP+4+VwVs3HPcjdWY9btFRKTD04xoDbh40gD+dsVEAD5bn83bX23HWktBaQW/fn05+SV1LAoSKSYehp8FaaNr76vw+sojR6Z/9CBsbMSUqVr2U0QkKiloH8CZo/tw65nDAfjhC4uZtWwbz87byHPzN/HMpxsBCIcPEEQ7d6+dVrWgSETQ/vB38MzZB86UN7NaYsFGKCs88PEiItIhKGg3wg0nH0FinJu87bEP1/LfZW7Q+x/fXcP89dkM/uXbZKzeVf8FuvSFod+omVaxX/N45Mjyu1Ngw9z6rxcOQUUZRy/8Ccz8XlOKIiIiPqag3QjGGObdfirnjevLmp0FrNqRX7XvUq+v+8E5q7ll5lIqQnXML24MTHusZtqmT2p+Li+q+fmLJ2t+Xv1O9Xa4orpPvDHN6SIi0iEoaDdSSudYHr1sAjOvO47pxw+stT9z2z5eXbyVLXuL675AYk+YdE3151xvNHi+96h6WVHtcyK9eEn1dmTQNvoViohEC/3Fb6LJg7px93mjmHndcTz5vUkEAzUnervuuYXc9OKS2icaA+c+7EaTR1r4T/f+x2GNz4QNubnLQUFbRCSK6C9+M00e1I3TRqax+t4z+eXZI6rS1+wsYNaybezKL6n7xD7ja6fV2X9tYMsXdV8jHIqoaTc8L7qIiHQcCtotFBMMcO1JR7D2vrMY1z+lKv3HLyxh3to97MjbL3h36QNXzYbIVUmfrWvBEAtPnVb3l9ZoHlfQFhGJFgrarSQmGGDm9ccx7/ZTmH78QBZszOE7T37OtMc+IbeojD+9u6Z6kNrhx8NdOW7ylfqseKP+fV88qT5tEZEopL/4rSg+Jkjfrp341TlH0j0xDoCd+0o59Y8f8cj7X7Ngw35ziE/+AdyVC6fd3bQvmvuggraISBTSX/yDICYYYOGvTuOla48lJmDILnQBtqy+x8FOvBmu+A+MnNb4LymofC5czeMiItFCQfsgMcZwzODuzP7JFOJi3I95W24Jtr4pSIecBt/+F0y+rnFf8MJF3hc14VeYuwU2fdb440VEpF1R0D7IhqYl88WdbkDZL1/7iqe9qU/rdfYfmvYFTRmI9uh4ePrMpl1fRETaDQXtQyClUyxnjEoD4J43V3DdcwsbPuHWDTD9bfhRPY98RYpLhO3LYOviAx8brmhEbkVEpL1S0D5EHv72eIalJQEwJ3MneUUNrBDWuRsMPAF6DoO7D7AsZ856+PtJ8I+pEA5DeT3Ph4uIiO8paB8iifExvPiDY6ue5f503R7un72SfQda3hPgV7tcrTu2M3zz0fqPuycV7kuD4lxX+w57A9+2fwkr/1t9XEg1bhERP4pp6wxEk+5J8bx03XFMfSiDH77gmrNDIcuvzh3Z8Ikx8a7Wfed297nbYHj23PqPf/os2LXCbX/7X7VXAvvXeXDV280shYiItBXVtA+xhNggf/nOxKrPT36ygUWb9jbtIoOmwN15ZJz8Oty4GG5aAj9eBMl93f7KgA11L9256VMoL4Z92+Ff50NhdpPLISIih56Cdhs46vBUMn97RtXnCx+fx4WPzyMUtoTDlic/Xk9+Y5rNjYHuR7iad48h8LOVMPXOxmXivt6uxr3+Q1jyXDNL0gjlJfD6D90NgoiItIiax9tIYnwMs38yhbMe+RiARZv2MvbuOYzul8LnG3JYu6uABy4c2/QLn3wrjLoAti2Gkjx4++f1H7tnjXv/cqZbOSyxF0y4onXnM1/9Fix9wa0X3v9oGP8d6JRa+7iyIve9sZ1a77tFRDoYBe02dGSfLiz61Wkc9bv3ACgsC/G5N9Vp1t5iMlbvYt3uQq45cVDTLtxjiHsBDD8LCnfDrlXQZxw8flzt43dlwvuZbvuzx6B4L3zzERh+pqspxyZUH7t1kQu6iT0hPrnxedr4KWS+5s6/6J+19/+/PtC5B9y6rvHXFBGJMgrabax7UjwbHziHT77ewy9eWcZ2b1WworIKpj/tntNOig9yydGHNe8LUvq7V98J7vPtm2H+32DDR65ve3+7V7r3Fy+pTjv7IVcL7jGs9opkNy2Fbg3cVFTOAFde7N6LG+i/L9rjRr33GddgkUREopWCdjtx4tAefPCzdP720Toeef9rFm/Ordp323++4oNVuwgYw82nD2NYmqvhhsKWUNgSDDShOTshBdJvc6+KMijdB3lb3ONkGHjs6NrnNNTEvvY9GH85zH8Mjr/JjXTfsRz+dgJMf6v6uMY2uf/9JLeIipYcFRGpRUG7HekUF+Tm04dx8vCezP5qO7OWbWPnvlLATcgCMHv5DgA2PnAOP/uomJ5L5/LuLSc37wtj4iCmByT2qE67c4db+vN/v2rcNT64F3ZmwqKn3SImZz8Ia991+1bPhn4T6z937Xvw/IUkHPO3mumhMhf8RUSkBgXtdmjiYalMPCyVO88Zya78Eibf936tYx77cC25pZbcXQWt++WxneC4H8PAKdB7rGuyXvg0LH+leuBapJI8F7ABFjwBhx0Hi551n8sK4JWr3batY4WzZTMASMlbXTO9vKjhoL1vO3z+uJsNrvc4OPkXTSykiHQY1rq/Q526tnVODgk98tXO9UpO4L1bTuJ7xx1eI/3BOdWB7paXlvLcZxspqwhjrWXNznwWbcrZ/1KNZwz0HQ+BACT1ck3pP/4CfvIl/GwNnPl7OPdPcFIdwfKVq2DvBre96Jnq9DLv5mL3avjiKSjKqervDoTLal7jpe82nL//3gSfPuJmefvwd7D039V95yISXT79M/z+cNi3ra1zckiopu0DQ3olc8+00fz2vFEYY3jzy23c/NJSykMuUL26ZCuvLtnKr9/IrHHexgfOAaC0IkR8TJDFm/eyans+3zmmmYPaUr0bh2Ovr047+XZY9aarmT9+HBTsbPga+7bCW7e4l6dbzn6LnWz8GDZ+AqmD4M2bYdBJMPR06Dnc7S/Nr3n86ze4x9WGnta8comIf1VO0Zy3Fbr0bdu8HAIK2j5ivMFZ547ty7lj+/L6Ox/Qa+hYVu/I59+fb+br/ZrKB97+FsnxMeSXVnDxUf15eVEW4JYIXXDnqfRKTqj1HU0WjIFR57vty192AXXgia5J/Z3boeLAC5j03FPHGt/PnFO9/fUc+N+dEIyH0+6u+5qldSysYq0L/gNPrB7Y9t5vYcAx7nE2EekAomvQqoK2j3VNCHD8ET04/ogeTD9+IJuyi5iTuYP7Z6+qOia/1C0OUhmwK32+Podvjmvlu9LIR7UmXeVeRTmQ9QWseQdMEHoMhdm3Nu/6oVKYc0fd+7YtgS1fuNXRjvQeS1s5y03jeu6fXV4APnnYvd+dBx//0QX1775W81rbl0EwDnod2bx8ikgbiI4uMgXtDsIYw8AeiVx38hFcd/IRhMKW3fmlvPnlNnbnl/L3uetrHH/ji0u48cUlAEwd3pPc4nLOH9+Py485jJhgKw516NwNhp3hXpWOuQ52rYTuQ2HbYlbMe4eRE451j42tz2je98z7P/f++ePuvc94SErz9j0KQ06DjAeqj3/ydMha4Lbf+BEcdyP0GuE+//0k936gZVFFRA4xBe0OKhgw9E5J4PtTBgNw25kuIFWELT98YTFfZuWyK989Tvbh6t0ALNmcy12zXL/46SPTuO3MEcQFA1z73EKmje/H9ScPrmqib7HKWuyAyexKK2LksHQY9o3q/TnrYc0c9zrxZhdwN89z+0ac6wLywqfqv/72pTWv9efRNfdXBmyAJc/D0hdh9IVw7A3V6YV7IKGr6wKoS8k+N2lMctoBCitSh6UvwtBvQGL3ts6Jv0XZnA4K2lEi4E3AEhcwPHnlpKr0nMIyFm7M4drnFtU4/t0VO3l3RfWgslXvrOL376xiYPfOJMQGmXHtsWzPK2FQj0QSYoNYa7l/9irOGt2bCYfVMbd4U3Ub7AJoZRAdfLLroy7YVR0kT74Vti52o9STerrpVStr3E1lQ/DVTPeq9OARMOG7bia4d38Np/waOnd3k9GMuRj+eqw77so3IX8HvPp9hvY9C8YMgPguYALN/4McKoev33XT0B6KP0pfzoScDe5JgUPt7hQ44Sdw+j0H7zts2M09cPT3IXXgwfuexsrZAK9fD4NOhitntXVuOoYoeYJEQTvKdUuM4xujeleNNF+3u4DOcUFemL+ZGV9sYfyArry3sjp4b8wuAmD8Pe9WpQ3tlcRZo3vzxNz1PDNvI1/88jSuefYLrjx+YOv2mxtTs1ab3BtGnO1elUZ80z2m1vUw+PhhOOwY6HcUzP8rDD8HPvuLC1DhRqyiBjVXQPvg3urtj/9YvR2xtnm/bbPh/2ZX7zvtbhcwinLcd097zNXOB0xueLrWJc+5kfPn/QUmeo/AWeumnj38hNYP5K/+wL0f6qAd9p7f//SRgxq0kwo2wqL/g03z4AcfHLTvabSQ95hjvla/aznv/4VwRdtm4xBR0JYajuiZBMDPzxjOz89wj1jtzi+lpDzEywu3kJVbzKuLt9Y45+tdBXz9wVoAyirCfPefn/NlVh57i8rYnOOC/HFHdOeuNzL5/pRBnDCkB898upGbTh1KXEwrTxVw2DHV25GTrlQ+U37+X93AtM3z4PAT3f/oZYUQn+QeR9vztQuypQWQ0g+ePqtl+Xnv7pqf3/hR9fa1H7mFV/400tW4hp8Ny/8Dk3/gAja4CWs+/7sbmb/5M/cc/LTHXB999lrXClCU41oCTr4Neo+pnphmz9dutrrK0f2HUlmhmxp30zw3K159q7eVFx2iDHm1sIqyhg9rrH+eBd0Hu99Fs7Jja75LyzX2RtznFLTlgHomuyBwyzdcEP/jxeMwxrA9r5j/LMriof/VnCntyyw3gGvd7sIak8AA/GTGUkb17ULmtn0c0SuRw7olYq1lw55CcgrLGNIziZTOsQe3QDFxMDjdbQdjqlcx6zbYvSL9JoeqO/myAgjEwI6voPsQV9tdOculFe+FhK7s+mwGvbrEwUm3wru/qdl3vr8nIqaf3fCRe0HNc3Z86d4fHgFHnue237zFjaTf39f/c+8/+ABe+LabzQ6g5BEYcrrrux9xjst318PdLHfFe92yqE2x8r8QDtV/M5CXBX8aBSf81E18Mf5yOO//4LXr4Zjrof9R1ccV7Gradx9I7mbXklH5TL/H1DUjX0tsnudezQ3aoVa6eejoCvfUnGZ5f6X51cE6pJq2SJ0qB6P1SenEj08ZyvenDMYYVyP/a8Y6BvdIJGAM97y5gsS4IDHBAHnF1XfBmdv2AXDzS8uqLzono2rzN+eO5L2VO3no4nH0SUng3RU7XU19Via3nD6MnsnxLNq4l+OH1PyfOaewjN+9tYJ7po0mKb6V/mkHgtXbCV3ce2Rt/qjpNQ5fsW8AvdLT3Yfvv+uC2+rZ7o9L7zHufe6D7hG14ibOWrfS6/usK2BH+scpNT//9yfV2xn/r+Fz925yz9eveYfjY7rAslS3ilv3oe5xuvl/qx4QmDnNPRp36l01B/oN9Z4U+PTP7n3pC+69cszAxc9AUTa89bOa312w2z2D33VAzfR374IVb8BPltad5wX/cBPw9BwOfx7j0vYb+R9ob7Wwiv1+hx/8zv270BML1VbMgpnfhavegcPrWFIY4P7+1dvt7Xd8kChoS4slxLrA1j+1M//vW2Oq0q/21gG31jJr2TZ27SulZ3I8z83fRE5hGeWhMFl7i2td7543VwBw/AO1+x437Clk8sBu/H3uemZedxx9UhIY0K0zAH/9cC2vLt7KmH4pXHVCE9cgP1gCQTjy3Jpp333VvVeUuRpXvOuSoCQP4pLcdIwVJW7A1Kq33Axxeze6gNpnrJsT/qjpbu72rAXu8bkeQ9311symRR4ZW7UZV54Le3PdtLTrPoAFf6957Io33PtXL9dM/3pO7etWBm6Al6fX/d0PDanejklwYxFSB8HS513aXybDt5+F5a9C9yNg3KVuBH99q9CFQ66Z/qPfk1BS2XpjYfuX7ucI7mcWjK0eI1Be7AJov4nuBqvypiwvC7r0a/lYgm1L3NMMib1qps990L1vmue6N/odVb1v5ZtQuLt6roGq8oVdeSJvLBtSVuT+vTV1ju5dq1y3zdRfHtqR2hs/du/bl9YM2uGwy8f+eQkdIGgvecGdM/47Lc/bzhVuyePKG/lDSEFbDjpjDNPG96v6fP6E6u28onLuefFDJo4ezsdr9vBO5o4Gr7Vkcy5LvGVLv/336pnULpzYn90FrvYSCvuknzAmzr0qJaS498ia5qjz62+GPvZ64PqaaXs3QXyyez4+VAHr3ndTzIbKXAAr3uv6wRf+090YvHdXxPd3dY/TpfRzAXm3N0lPMP7AtfvWVlHiBt1Frvm+Z3X1iH1wLQj1zbh3d0qNj1XT5OxcDn+f4prrcze7YBmMq7+5eucKGHMRPHV63fs/vB+m/AwWP+sGFg6Y7IL9B/e5P+qFu1wrRd8JUJJbPdPf+d58Atlf1+zXrhxDcdU78PR+s/Z1HUDyvg1Auvv82rXuhunWDa6Lxobd66Pfw9Q73c1W6sDqf1dPne7Kf+fO6i6hz5+A2b+Ai56G0RfUXcZ/TYOCHe5Jhn9MdZMRHeG15ljrJiPqPgTiEt2NUqjUbUcqzXfHVga5cJiYctfiRkUZzLoRTvq5u/lc96F3Y1HPDcJTp7sbrcjHM8HVtNd96LqvTrip9nlv/NC97x+0F/zD/Vv4xr21z6lLqMJN2TxwCkx/s3HntCJj2/lAiEmTJtmFCxe22vUyMjJIr2y+9LmOUpb9y1ERChMMGLbmFvP6kq2MH5DK0i17yS4s4/2Vu6oGtzVGWpd4juzThYAxHDe4O3sKS4kLBrhwYn9SE+NI6eRqYNkFpcTFBEhOaFl/uu9+J0U5LsDXYf7sGRx7xrfdwjGVfyc2zHXN0JUT11QOOAt4AwpLC9yjTOMvh2FnupuE+GQXVEpy3VrrWxe5R+aS+0B5Icx9yK0gt2ulGxg47jKY+wfX75676eD/DKLB4Kmw/sPqz/2Ocr+HpugzzgVocL/z437sfk+VznoQVr9d/T13bHW//60L4b8/db//az8CLMy4AvZlwXVzXctJZVfKsDPd7Ingxp2sz4Cxl7hBmMl93GvF63Xn7xv3uemOAW7f4m7G9m1141Syvqi+8frRAteatWaOa6X47C8uPXUgXPK8e/Jk3zYXzA8/HkZdALsyXfqmedBzBDw63p1zdx6Ew2TMnduq/98bYxZZayfVuU9B2786SlmaU46C0gpKy0MUl4eYu2YP63cXUFwe4oXPN9c4rl/XTmzNrd0E35BJh6fSKS7Iz78xnMLSCo4e1A2Dm7DGGENZRZg1O/MZ3S+l1rkd5XcC7aQs4bB7hj4YC/k7XY0odaCb+a5zd3cD0bm7+wMdroDyEldbK9zjarnzH4fB6SzeHmbi+DHw/j0uAIALEKf8Gl691tUycze5Z/0Turp92xZXj/aPSXCPGJbmw+WvuGDyyZ+gdF/rl7nfJNdEHipzwUbar26DIWc96wd9l8FX/qXVLttQ0FbzuPhSUnxM1WCzyFXL7vvWGEJhi7WWoDehzJItuWTtLeaj1bvZV1JeY9KYuizctBeAj7/eU2tf5QIsAL84YzgVIUv/1E5szC7kx6cMqXW8tFAgQNUKwslp1c/pn/qbhs/r3A16DnOLxQD7MjJcrenqd2of+8N5dV8jbSRMuKLuff0mwpTqleoIVbhnrpPS3A1GebGrxZUWuFH9hx/naol1rRMfKnfz8ocr3AC9Ln1q7rfW9cWGypmb8SEnHX+0u3kwQdfXHdvZDRYMxrpJfhJ7uqcOknq77o9jrnf96Gmj3AC4D/+fOz5U7o7vNwHikt3NTN/xMP4K14wdiHHjLOKTXVdE7mY3OU1l+TJfg7N+D2vfhzd/6o4raeJAusRerjsmdaArT+8xLl+Fu90YiiGnw9p3a5/Xubvrfqis+cd3afkNVI9hrsWnKXLc9NAlCT1b9t1NoKAtHY4L1tX9YRMPS2XiYamcV8dEL/tKyskrKic5IYasvcX8L3MHj3rPnMcEDJ3iguSXVD9KUhmwgVqPs/2fdx7vvMXxR3QnJhjgpKE9OLJPF77emc/G7CKG9EpiyeZcfn7GMErLwzw3fxO3nzWC2APM955bVEZSfEzrzgsvrScYU3MsQpwbHElMPIy/7ADnel0ygbjaARuqB1wFYwkH41xrQCdv1sGUfjWPTe7t3vtOcO8XPOHe+02sPubymdTp2Otrp1V2nZz/19r7xlzk3o+60r0iFeyGte+5JXUTe7gWk0DAvZfmQafUA7fknB3R9G6tu/kZcnp1V0x9yktct03lbITlJa6vP7aTG1DZe4ybgAlcH3zkQL6yourfXVGOW2o4Z71rGu8+FHLWufkPRk5zMxamjYS4JHZ9vpSRDeeq1TQ7aBtjBgD/AnoDYeAJa+0jxphuwEvAQGAj8G1r7V7vnDuAa4AQcJO1to5hpiKHTpeEWLp4/dhdO8cxul9K1fPo4Ea+z1uXzai+XUiMj2H51jy27C3m3RU7CYctobBlyZa97CuuoLg8VHXevHXZAMxds7vO7/3P4upV1576ZEPV9olDevCdYw4jLhhgX0k5X2bl0TM5ngfnrOaKYw9j+vEDKauwjOxbc9SqtRZjDDmFZcTHBEhsrUfeRJojqWfNm5XKQBsIVN9wNIUxNRcdakhsQvVAu8rPlYacWvPY/UfeVwZscDcsnbvVXO0vbZR7Qc21Eg6hlvyfXQH8zFq72BiTDCwyxrwLTAfet9Y+YIy5HbgduM0YMxK4FBgF9AXeM8YMs9aG6rm+SJszxnBCxPPgEw5LZUI9tfZw2DJ37kcce8IUcovKKSitYPWOfDJW72LRpr1szS2mtKJ6ko/uiXFkF9YctfzJ2j18srZ2szzA8/M38/x812d/ztg+rva+p4hvTejHSwu3cNnkw3hxwWbSusTz0S+m8sbSrXyZlcc1Jw5iUI9ESivCVY/nVaoM9nsKSnlz2TauPH5g6y0KIyKtrtlB21q7HdjubecbY1YC/YBpVD2TwLNABnCblz7DWlsKbDDGrAUmA58h0gFULsqSEBukd4oLjkN6JXHO2DqaPHEBc2tuMbHBAGt25pO5bR+DeiSydEsuFaEwifExfL4+h8/WZ9c6960vq+esfmnhFgBeXOAC+s59pYz4dXXfbeTgvClDexAbDLC3qIyCkgp2F5Tyo/Qh3Pf2SgDC1o0RyCksY3tBmMLSCt5buZNTj0wjMS7I6p35DOmZpGZ6kTbSKm1oxpiBwATgcyDNC+hYa7cbYypnEegHzI84LctLE4lKxhj6p7rmuLQuCUwZ6gaznDGqd4PnlVWE2Z5XTH5JBXnF5azZmU9RWYjN2UV8sHoXu/NL6ZOSQHJCDGt2FtQ4t67BdZUBG9zENpWT2wDc8UndPVhpXeKZPKg7OYWlHNm7C0f26cK4AV3ZW1TGsi25FJeF6NO1ExMO68ph3ToTGwwQClsKSiro0immRm3+v8u2UVRWwSVHH1bnd4lItRY/8mWMSQI+Au6z1r5qjMm11naN2L/XWptqjHkM+Mxa+7yX/hTwtrX2P3Vc81rgWoC0tLSjZsyY0aI8RiooKCApKanVrteWOkpZOko5oH2WpbIJHKCo3BIbhMIyy+b8MH0SA/xlaSndEgwVYSgNub8HW/LDFFfA6O5Blme3rAcrMRYCBvIjegLSB8SQV2pZszdEoTeR1beGxHJ4lwAzVpdx1qBYBqcEeWFlKWN6BDnlsFgMsK/MMmtdOSO6BZjcO4bYQPW0ujsKw6TGG+JjajfvFxQUkLErjl6dAkzu49/+/vb476u5VJb6TZ069eA8p22MiQXeBOZYax/20lYD6V4tuw+QYa0d7g1Cw1p7v3fcHOBua22DzeN6Trt+HaUsHaUc0LHLUhEKk1NURlFpiIpwGGMMhaUVfLjKDbbbW1TGpuxC4mICzMl0j9X1So4nNhho8rPyTZXSKbZqfvvEuCC/POdIfj97FftKKhjSK4kRSaW8ud7tf/zyiZwwtAddEmIpKQ8RHxPAGIO1loLSCpITYtlXUs7vZ6/ix6cMIaewjBG9u1Q9Qtha9haWERsTaNI8+R3535eftXZZDspz2sbd3j4FrKwM2J5ZwJXAA977GxHp/zbGPIwbiDYUaGAJJBFpT2KCAXolJ0ByzfSx/bs26ToVoTDb80ooKgsREzTMXbOb/qmdyS0qY8GGHMpDYQIBQ1ZOMZ3jg+wtLCM+JsiaXfnkFpUTDBhSO8eyp6C66h65IE1hWYg7X1te9XntrgLWRiwmdsMLi2vkJzLgA4zs04UV290zvx+t2V01P/6gHokcPTCV2GCAr7bmERsMMLZ/CscM6saQXsn0T+3Emp35fLFxLxdO7EfXznGUVoSICwbYnV/K+Y99yp8vncDRA1MxxjDh3nc5rFtn5t46leKyEFv2FrF1bzEWyykj0mgvyirC7C4opV/XepZXbWOFpRVc8+wX/Pa80QzvnXzgE3yuJe1EJwDfBb4yxiz10n6JC9YzjTHXAJuBiwGstZnGmJnACtzI8x9p5LhI9IkJBqoWeYHqNdwBLp40oK5TaqgIhYkJBigPhckrLic2GCC3qIzOcTFszikkPibo3RRUsG5XAV06xfLHOSu56sQjWLe7gIUb9zKqXwolZSHyS11NPGP1LsoqwpRWhGtMkxu5oM2GPYVs2FNYIy+LNu3l6U831srjvW+uqJqIJ2DcAD+oOV8+wOacIu56YzkvLdxCSXn1kwXfOeYwPluXTWzQ0CelEx+t2c2tZw6nfHcFuUu20j+1E5uyi1i8eS/3ThvNnoJSeiS5iVsqB0TuyCvh8w3ZNeb9LykPURG29dburbWELTVaFe7+byb//nwzy397RuutnteK5q/PZv76HH731gqeu+aYA5/gcy0ZPf4J9c7ozql1JVpr7wPua+53iohUjlyPDQaqAlXlHPKVa7/vP8XskNBm0tNH1HvNyH7/yC7DHftKSIgJsjW3mJLyEHsKSunVJYGCkgrmrctmwYZsJh6WSq8u8WRu20fPpPiqwYHdk+LZuKeQ7MKyGjX5/T37We351f8dMeK/cjDhH97xJvNZtLTGsftP3ZsYF2RM/xTmr3dLv/7i5S85ZnA3tu4tZn3ETceovl0Y0bsLJeUh+qd2YsveIt7+yi3Y86tzjmTpllxOGNKjKi9vf7WdgpIKJg1MZUy/lKqfV3koTFFpiJTO7newc18JizbtpXdKAhMPq/1MdkFpBb967SumpBx4jfOtucVVXSz1qfDuiA7UfVERCrO3qLzq34hftb/bJhGRQyxyNHvkdp8U1yScmhhX65yThjV96kprLeUhy+6C0qq15tfuKqBncjyl5SHmrtlN366dWLk9n5ROMcTHBsnclsfRA7vxh3dWU1hcwlnj+rMt161ulhQfQ25xGZ+uzSY+JkBpRZjCshCLNu0lNmgoD1nKQuE6nxrI3Lavam37/f3uLfdEwZsRjxbe+sqXtY4b3DOR9bvdjUByfAz9Ujuxakd+1f4RvZM5eXhPsPD3uesZ0y+Fr7a6qU7ndza8vfMLtueVcPrINAb3TCSlUyxJ8THeExIl/OzlZVx69ADOHduXkvIQEw9PpbC0glU78jl9ZBp5xeV8vdN9X9AYNuwp5MusXL45ti+BgKm6GdudX8qNLy5m/voc1vzuLOJiat8EVLbgtHcK2iIih4gxhrgYU6N/ePyArlXbg72ugm/U8djftPH9vAFPY2vt2184bAlZW1VDDYUtG/YUEBt0gT2/pJwBqZ1JTYyjuDzE0s25LNy0l17J8aza4QL5im372JVfSt+UTuQUlbF2V0Gt7wlG3ODkl1awK7/mEq6rduTXCOKVARtgR5Flxyo32KByDEFdZnyxhRlfbDlgmd9ftYv3vev9ZMZShqcls3pnPoN6JNbo1vjJjCVkbttHXnE5Z4xKo7g8zH+XbQPc7+LEIT3omRxPeSjMeyt30rVTHHecPYLconI+35DN4B5J5BaXMydzB/932QTySyqqavuHgoK2iEgHEwgYAhG9l8GAYUivugdpxQYDnDSsZ5NaDiK7Exo6pqgsRFlFmBXb99E/tRO5ReV0S3QD9F794HPGjh5NdmFp1WDDzG155BWXM2VoT77eVUCXhBjW7S7gvZW7mDyoG7lFZVXdBb2S40lKiGH97kLSh/ckY3XNKYNXezXw/cchzF6+o2p75sKsGvuWbsll6ZbcWmV5J3NHrTSgahKj0w+P4bRTGvxxtBoFbRERaZLGTHVrjCExPobEeKqmAj68e/X+yb1jSB/d8ERCTVF5k1A5735ZRZiw9xhfblEZ/bp2prDMTS3cOyWBr3fm0ykuht35peQUljKqbwoVYcvOfSWEw5YN2YUc1q0za3cVsCnbDU78bF02A3skMiwtidlf7eC0kb0IGMPkpL2tVo4DUdAWERHfq7xJqFTZb50QG6wasNgpLkiPIW478qmF5njk0urtjIyMFl2rKdp/r7uIiIgACtoiIiK+oaAtIiLiEwraIiIiPqGgLSIi4hMK2iIiIj6hoC0iIuITCtoiIiI+oaAtIiLiEwraIiIiPqGgLSIi4hMK2iIiIj6hoC0iIuITxtpDt3h3cxhjdgObWvGSPYA9rXi9ttRRytJRygEqS3vVUcrSUcoBKktDDrfW1rnAebsP2q3NGLPQWjuprfPRGjpKWTpKOUBlaa86Slk6SjlAZWkuNY+LiIj4hIK2iIiIT0Rj0H6irTPQijpKWTpKOUBlaa86Slk6SjlAZWmWqOvTFhER8atorGmLiIj4UtQEbWPMmcaY1caYtcaY29s6PwdijBlgjPnQGLPSGJNpjPmJl97NGPOuMeZr7z014pw7vPKtNsac0Xa5r80YEzTGLDHGvOl99ms5uhpjXjHGrPJ+N8f5uCw3e/+2lhtjXjTGJPilLMaYfxpjdhljlkekNTnvxpijjDFfefseNcaYdlKWB71/Y18aY14zxnSN2Ncuy1JXOSL2/dwYY40xPSLS2mU5vDzUWRZjzI1efjONMX+ISD90ZbHWdvgXEATWAYOBOGAZMLKt83WAPPcBJnrbycAaYCTwB+B2L/124Pfe9kivXPHAIK+8wbYuR0R5bgH+DbzpffZrOZ4Fvu9txwFd/VgWoB+wAejkfZ4JTPdLWYCTgInA8oi0JucdWAAcBxhgNnBWOynLN4AYb/v3fihLXeXw0gcAc3DzbfRo7+Vo4HcyFXgPiPc+92qLskRLTXsysNZau95aWwbMAKa1cZ4aZK3dbq1d7G3nAytxf2in4QIH3vv53vY0YIa1ttRauwFYiyt3mzPG9AfOAZ6MSPZjObrg/md+CsBaW2atzcWHZfHEAJ2MMTFAZ2AbPimLtXYukLNfcpPybozpA3Sx1n5m3V/Yf0Wcc8jUVRZr7f+stRXex/lAf2+73Zalnt8JwJ+AW4HIAVTtthxQb1luAB6w1pZ6x+zy0g9pWaIlaPcDtkR8zvLSfMEYMxCYAHwOpFlrt4ML7EAv77D2XMY/4/6nDUek+bEcg4HdwNNeU/+TxphEfFgWa+1W4CFgM7AdyLPW/g8fliVCU/Pez9veP729uRpXSwOflcUYcx6w1Vq7bL9dviqHZxgwxRjzuTHmI2PM0V76IS1LtATtuvoRfDFs3hiTBPwH+Km1dl9Dh9aR1uZlNMacC+yy1i5q7Cl1pLV5OTwxuCazx621E4BCXDNsfdptWbz+3mm45ry+QKIx5oqGTqkjrV2UpRHqy3u7L5Mx5k6gAnihMqmOw9plWYwxnYE7gd/UtbuOtHZZjggxQCpwLPALYKbXR31IyxItQTsL169SqT+uKbBdM8bE4gL2C9baV73knV6zC957ZRNNey3jCcB5xpiNuG6JU4wxz+O/coDLW5a19nPv8yu4IO7HspwGbLDW7rbWlgOvAsfjz7JUamres6hudo5MbxeMMVcC5wKXe82r4K+yHIG7KVzm/f/fH1hsjOmNv8pRKQt41ToLcC2HPTjEZYmWoP0FMNQYM8gYEwdcCsxq4zw1yLuDewpYaa19OGLXLOBKb/tK4I2I9EuNMfHGmEHAUNwgiDZlrb3DWtvfWjsQ93P/wFp7BT4rB4C1dgewxRgz3Es6FViBD8uCaxY/1hjT2fu3dipu3IQfy1KpSXn3mtDzjTHHej+D70Wc06aMMWcCtwHnWWuLInb5pizW2q+stb2stQO9//+zcINrd+CjckR4HTgFwBgzDDcQdQ+HuiytPequvb6As3EjsNcBd7Z1fhqR3xNxTSlfAku919lAd+B94GvvvVvEOXd65VtNG4y4bESZ0qkePe7LcgDjgYXe7+V1XHOZX8vyW2AVsBx4Djf61RdlAV7E9cWX44LBNc3JOzDJK/864C94E061g7KsxfWTVv6//7f2Xpa6yrHf/o14o8fbczka+J3EAc97eVsMnNIWZdGMaCIiIj4RLc3jIiIivqegLSIi4hMK2iIiIj6hoC0iIuITCtoiIiI+oaAtIs1ijEk33qptInJoKGiLiIj4hIK2SAdnjLnCGLPAGLPUGPN349Y2LzDG/NEYs9gY874xpqd37HhjzHxTvY5zqpc+xBjznjFmmXfOEd7lk0z1+uIvtMXaxyLRREFbpAMzxhwJXAKcYK0dD4SAy4FEYLG1diLwEXCXd8q/gNustWOBryLSXwAes9aOw81Rvt1LnwD8FLem8GDcXPMicpDEtHUGROSgOhU4CvjCqwR3wi2kEQZe8o55HnjVGJMCdLXWfuSlPwu8bIxJBvpZa18DsNaWAHjXW2CtzfI+LwUGAp8c9FKJRCkFbZGOzQDPWmvvqJFozK/3O66h+YwbavIujdgOob8pIgeVmsdFOrb3gYuMMb0AjDHdjDGH4/7fv8g75jvAJ9baPGCvMWaKl/5d4CPr1nHPMsac710j3lsrWUQOMd0Vi3Rg1toVxphfAf8zxgRwqxb9CCgERhljFgF5uH5vcEta/s0LyuuBq7z07wJ/N8bc413j4kNYDBHxaJUvkShkjCmw1ia1dT5EpGnUPC4iIuITqmmLiIj4hGraIiIiPqGgLSIi4hMK2iIiIj6hoC0iIuITCtoiIiI+oaAtIiLiE/8fg8/aDg59jFIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1602/2000:  train Loss: 168.4804   val Loss: 220.9861   time: 3.11s   best: 219.4391\n",
      "Epoch 1603/2000:  train Loss: 170.0358   val Loss: 224.7449   time: 3.11s   best: 219.4391\n",
      "Epoch 1604/2000:  train Loss: 168.7569   val Loss: 223.3811   time: 3.11s   best: 219.4391\n",
      "Epoch 1605/2000:  train Loss: 167.8928   val Loss: 221.9626   time: 3.10s   best: 219.4391\n",
      "Epoch 1606/2000:  train Loss: 168.5261   val Loss: 221.9713   time: 3.14s   best: 219.4391\n",
      "Epoch 1607/2000:  train Loss: 168.9102   val Loss: 219.8018   time: 3.20s   best: 219.4391\n",
      "Epoch 1608/2000:  train Loss: 168.1835   val Loss: 223.2194   time: 3.13s   best: 219.4391\n",
      "Epoch 1609/2000:  train Loss: 168.5036   val Loss: 221.1259   time: 3.11s   best: 219.4391\n",
      "Epoch 1610/2000:  train Loss: 168.4469   val Loss: 220.3128   time: 3.11s   best: 219.4391\n",
      "Epoch 1611/2000:  train Loss: 168.7878   val Loss: 221.9288   time: 3.11s   best: 219.4391\n",
      "Epoch 1612/2000:  train Loss: 169.2542   val Loss: 224.5317   time: 3.10s   best: 219.4391\n",
      "Epoch 1613/2000:  train Loss: 168.0462   val Loss: 222.1692   time: 3.10s   best: 219.4391\n",
      "Epoch 1614/2000:  train Loss: 167.9901   val Loss: 223.2699   time: 3.17s   best: 219.4391\n",
      "Epoch 1615/2000:  train Loss: 167.3668   val Loss: 224.2976   time: 3.18s   best: 219.4391\n",
      "Epoch 1616/2000:  train Loss: 166.8794   val Loss: 222.6224   time: 3.11s   best: 219.4391\n",
      "Epoch 1617/2000:  train Loss: 167.1105   val Loss: 221.9436   time: 3.11s   best: 219.4391\n",
      "Epoch 1618/2000:  train Loss: 167.9300   val Loss: 222.2608   time: 3.11s   best: 219.4391\n",
      "Epoch 1619/2000:  train Loss: 167.6293   val Loss: 223.2630   time: 3.11s   best: 219.4391\n",
      "Epoch 1620/2000:  train Loss: 167.7438   val Loss: 222.8016   time: 3.09s   best: 219.4391\n",
      "Epoch 1621/2000:  train Loss: 167.9806   val Loss: 223.9960   time: 3.11s   best: 219.4391\n",
      "Epoch 1622/2000:  train Loss: 167.8276   val Loss: 224.2854   time: 3.21s   best: 219.4391\n",
      "Epoch 1623/2000:  train Loss: 168.2084   val Loss: 223.6774   time: 3.12s   best: 219.4391\n",
      "Epoch 1624/2000:  train Loss: 167.6683   val Loss: 224.3524   time: 3.10s   best: 219.4391\n",
      "Epoch 1625/2000:  train Loss: 167.3008   val Loss: 221.0744   time: 3.10s   best: 219.4391\n",
      "Epoch 1626/2000:  train Loss: 167.2606   val Loss: 223.7171   time: 3.10s   best: 219.4391\n",
      "Epoch 1627/2000:  train Loss: 167.8579   val Loss: 223.6931   time: 3.09s   best: 219.4391\n",
      "Epoch 1628/2000:  train Loss: 168.5504   val Loss: 222.5540   time: 3.09s   best: 219.4391\n",
      "Epoch 1629/2000:  train Loss: 166.8652   val Loss: 220.7613   time: 3.14s   best: 219.4391\n",
      "Epoch 1630/2000:  train Loss: 168.4922   val Loss: 221.3140   time: 3.24s   best: 219.4391\n",
      "Epoch 1631/2000:  train Loss: 167.5982   val Loss: 221.4751   time: 3.24s   best: 219.4391\n",
      "Epoch 1632/2000:  train Loss: 172.1646   val Loss: 220.0718   time: 3.22s   best: 219.4391\n",
      "Epoch 1633/2000:  train Loss: 168.8537   val Loss: 220.7315   time: 3.22s   best: 219.4391\n",
      "Epoch 1634/2000:  train Loss: 169.5619   val Loss: 224.4446   time: 3.21s   best: 219.4391\n",
      "Epoch 1635/2000:  train Loss: 169.8093   val Loss: 219.9212   time: 3.13s   best: 219.4391\n",
      "Epoch 1636/2000:  train Loss: 168.6048   val Loss: 221.8926   time: 3.12s   best: 219.4391\n",
      "Epoch 1637/2000:  train Loss: 167.3451   val Loss: 221.2709   time: 3.21s   best: 219.4391\n",
      "Epoch 1638/2000:  train Loss: 166.4724   val Loss: 220.3351   time: 3.12s   best: 219.4391\n",
      "Epoch 1639/2000:  train Loss: 167.9075   val Loss: 220.8693   time: 3.10s   best: 219.4391\n",
      "Epoch 1640/2000:  train Loss: 169.2084   val Loss: 219.6465   time: 3.10s   best: 219.4391\n",
      "Epoch 1641/2000:  train Loss: 168.1693   val Loss: 219.1165   time: 3.09s   best: 219.1165\n",
      "Epoch 1642/2000:  train Loss: 167.5277   val Loss: 219.7259   time: 3.09s   best: 219.1165\n",
      "Epoch 1643/2000:  train Loss: 167.0696   val Loss: 219.9705   time: 3.09s   best: 219.1165\n",
      "Epoch 1644/2000:  train Loss: 168.7265   val Loss: 227.2665   time: 3.15s   best: 219.1165\n",
      "Epoch 1645/2000:  train Loss: 171.4006   val Loss: 220.6243   time: 3.17s   best: 219.1165\n",
      "Epoch 1646/2000:  train Loss: 167.4391   val Loss: 221.6061   time: 3.10s   best: 219.1165\n",
      "Epoch 1647/2000:  train Loss: 166.9581   val Loss: 222.2121   time: 3.10s   best: 219.1165\n",
      "Epoch 1648/2000:  train Loss: 167.6129   val Loss: 221.2008   time: 3.10s   best: 219.1165\n",
      "Epoch 1649/2000:  train Loss: 166.6444   val Loss: 223.2462   time: 3.09s   best: 219.1165\n",
      "Epoch 1650/2000:  train Loss: 167.4496   val Loss: 222.5039   time: 3.09s   best: 219.1165\n",
      "Epoch 1651/2000:  train Loss: 167.5789   val Loss: 220.2018   time: 3.10s   best: 219.1165\n",
      "Epoch 1652/2000:  train Loss: 166.8222   val Loss: 223.0091   time: 3.19s   best: 219.1165\n",
      "Epoch 1653/2000:  train Loss: 167.7575   val Loss: 223.4285   time: 3.13s   best: 219.1165\n",
      "Epoch 1654/2000:  train Loss: 166.4687   val Loss: 220.8560   time: 3.10s   best: 219.1165\n",
      "Epoch 1655/2000:  train Loss: 167.2469   val Loss: 220.7379   time: 3.10s   best: 219.1165\n",
      "Epoch 1656/2000:  train Loss: 167.9520   val Loss: 219.8850   time: 3.09s   best: 219.1165\n",
      "Epoch 1657/2000:  train Loss: 166.6844   val Loss: 219.1118   time: 3.09s   best: 219.1118\n",
      "Epoch 1658/2000:  train Loss: 166.4117   val Loss: 218.9473   time: 3.09s   best: 218.9473\n",
      "Epoch 1659/2000:  train Loss: 166.9940   val Loss: 222.9615   time: 3.14s   best: 218.9473\n",
      "Epoch 1660/2000:  train Loss: 166.5909   val Loss: 223.2306   time: 3.18s   best: 218.9473\n",
      "Epoch 1661/2000:  train Loss: 166.3319   val Loss: 223.3192   time: 3.11s   best: 218.9473\n",
      "Epoch 1662/2000:  train Loss: 166.4633   val Loss: 222.2249   time: 3.10s   best: 218.9473\n",
      "Epoch 1663/2000:  train Loss: 167.0285   val Loss: 220.5233   time: 3.10s   best: 218.9473\n",
      "Epoch 1664/2000:  train Loss: 166.6066   val Loss: 219.9432   time: 3.10s   best: 218.9473\n",
      "Epoch 1665/2000:  train Loss: 167.5852   val Loss: 218.9625   time: 3.09s   best: 218.9473\n",
      "Epoch 1666/2000:  train Loss: 167.7613   val Loss: 222.0069   time: 3.09s   best: 218.9473\n",
      "Epoch 1667/2000:  train Loss: 166.9018   val Loss: 222.8438   time: 3.16s   best: 218.9473\n",
      "Epoch 1668/2000:  train Loss: 167.5511   val Loss: 218.0051   time: 3.17s   best: 218.0051\n",
      "Epoch 1669/2000:  train Loss: 168.2878   val Loss: 225.8213   time: 3.10s   best: 218.0051\n",
      "Epoch 1670/2000:  train Loss: 169.0238   val Loss: 221.8738   time: 3.10s   best: 218.0051\n",
      "Epoch 1671/2000:  train Loss: 166.6091   val Loss: 221.1420   time: 3.10s   best: 218.0051\n",
      "Epoch 1672/2000:  train Loss: 166.8380   val Loss: 221.9594   time: 3.09s   best: 218.0051\n",
      "Epoch 1673/2000:  train Loss: 168.2233   val Loss: 222.4256   time: 3.09s   best: 218.0051\n",
      "Epoch 1674/2000:  train Loss: 166.4791   val Loss: 225.1339   time: 3.10s   best: 218.0051\n",
      "Epoch 1675/2000:  train Loss: 165.8600   val Loss: 221.9485   time: 3.20s   best: 218.0051\n",
      "Epoch 1676/2000:  train Loss: 165.4106   val Loss: 221.6200   time: 3.12s   best: 218.0051\n",
      "Epoch 1677/2000:  train Loss: 165.8643   val Loss: 221.0819   time: 3.10s   best: 218.0051\n",
      "Epoch 1678/2000:  train Loss: 165.6128   val Loss: 222.9459   time: 3.10s   best: 218.0051\n",
      "Epoch 1679/2000:  train Loss: 166.0754   val Loss: 223.2942   time: 3.10s   best: 218.0051\n",
      "Epoch 1680/2000:  train Loss: 167.4538   val Loss: 221.2051   time: 3.09s   best: 218.0051\n",
      "Epoch 1681/2000:  train Loss: 165.9097   val Loss: 219.4634   time: 3.09s   best: 218.0051\n",
      "Epoch 1682/2000:  train Loss: 166.3595   val Loss: 222.5492   time: 3.15s   best: 218.0051\n",
      "Epoch 1683/2000:  train Loss: 167.0394   val Loss: 221.1813   time: 3.18s   best: 218.0051\n",
      "Epoch 1684/2000:  train Loss: 166.1368   val Loss: 220.3004   time: 3.11s   best: 218.0051\n",
      "Epoch 1685/2000:  train Loss: 167.7429   val Loss: 222.6352   time: 3.10s   best: 218.0051\n",
      "Epoch 1686/2000:  train Loss: 166.1560   val Loss: 221.8571   time: 3.09s   best: 218.0051\n",
      "Epoch 1687/2000:  train Loss: 166.9348   val Loss: 220.8786   time: 3.09s   best: 218.0051\n",
      "Epoch 1688/2000:  train Loss: 165.6992   val Loss: 222.9568   time: 3.08s   best: 218.0051\n",
      "Epoch 1689/2000:  train Loss: 167.0616   val Loss: 221.1563   time: 3.09s   best: 218.0051\n",
      "Epoch 1690/2000:  train Loss: 168.0159   val Loss: 220.2720   time: 3.18s   best: 218.0051\n",
      "Epoch 1691/2000:  train Loss: 166.5761   val Loss: 224.7857   time: 3.16s   best: 218.0051\n",
      "Epoch 1692/2000:  train Loss: 166.9183   val Loss: 220.8225   time: 3.10s   best: 218.0051\n",
      "Epoch 1693/2000:  train Loss: 165.7057   val Loss: 220.1483   time: 3.10s   best: 218.0051\n",
      "Epoch 1694/2000:  train Loss: 166.7342   val Loss: 218.9296   time: 3.10s   best: 218.0051\n",
      "Epoch 1695/2000:  train Loss: 165.6027   val Loss: 219.7979   time: 3.09s   best: 218.0051\n",
      "Epoch 1696/2000:  train Loss: 166.7049   val Loss: 222.5847   time: 3.09s   best: 218.0051\n",
      "Epoch 1697/2000:  train Loss: 165.7799   val Loss: 221.4401   time: 3.12s   best: 218.0051\n",
      "Epoch 1698/2000:  train Loss: 165.8762   val Loss: 221.7186   time: 3.20s   best: 218.0051\n",
      "Epoch 1699/2000:  train Loss: 166.9595   val Loss: 220.8543   time: 3.11s   best: 218.0051\n",
      "Epoch 1700/2000:  train Loss: 165.3673   val Loss: 222.4968   time: 3.11s   best: 218.0051\n",
      "Epoch 1701/2000:  train Loss: 165.6454   val Loss: 222.3851   time: 3.11s   best: 218.0051\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1702/2000:  train Loss: 165.3361   val Loss: 222.1442   time: 3.11s   best: 218.0051\n",
      "Epoch 1703/2000:  train Loss: 166.8502   val Loss: 219.4380   time: 3.10s   best: 218.0051\n",
      "Epoch 1704/2000:  train Loss: 165.0505   val Loss: 221.1316   time: 3.10s   best: 218.0051\n",
      "Epoch 1705/2000:  train Loss: 165.1874   val Loss: 219.8081   time: 3.18s   best: 218.0051\n",
      "Epoch 1706/2000:  train Loss: 165.7891   val Loss: 222.4770   time: 3.16s   best: 218.0051\n",
      "Epoch 1707/2000:  train Loss: 165.4094   val Loss: 221.3742   time: 3.11s   best: 218.0051\n",
      "Epoch 1708/2000:  train Loss: 165.6347   val Loss: 221.1254   time: 3.17s   best: 218.0051\n",
      "Epoch 1709/2000:  train Loss: 165.1174   val Loss: 222.9232   time: 3.23s   best: 218.0051\n",
      "Epoch 1710/2000:  train Loss: 165.6515   val Loss: 227.5419   time: 3.22s   best: 218.0051\n",
      "Epoch 1711/2000:  train Loss: 166.4317   val Loss: 222.8489   time: 3.22s   best: 218.0051\n",
      "Epoch 1712/2000:  train Loss: 165.8541   val Loss: 222.1343   time: 3.24s   best: 218.0051\n",
      "Epoch 1713/2000:  train Loss: 164.8511   val Loss: 223.9406   time: 3.35s   best: 218.0051\n",
      "Epoch 1714/2000:  train Loss: 166.3240   val Loss: 222.1046   time: 3.26s   best: 218.0051\n",
      "Epoch 1715/2000:  train Loss: 165.7060   val Loss: 222.4960   time: 3.24s   best: 218.0051\n",
      "Epoch 1716/2000:  train Loss: 165.5924   val Loss: 223.0499   time: 3.23s   best: 218.0051\n",
      "Epoch 1717/2000:  train Loss: 166.6211   val Loss: 220.7923   time: 3.23s   best: 218.0051\n",
      "Epoch 1718/2000:  train Loss: 167.7715   val Loss: 221.0149   time: 3.23s   best: 218.0051\n",
      "Epoch 1719/2000:  train Loss: 166.2204   val Loss: 220.9607   time: 3.23s   best: 218.0051\n",
      "Epoch 1720/2000:  train Loss: 164.9561   val Loss: 221.1708   time: 3.32s   best: 218.0051\n",
      "Epoch 1721/2000:  train Loss: 165.4860   val Loss: 221.9783   time: 3.30s   best: 218.0051\n",
      "Epoch 1722/2000:  train Loss: 165.2583   val Loss: 219.1510   time: 3.23s   best: 218.0051\n",
      "Epoch 1723/2000:  train Loss: 166.9802   val Loss: 222.3854   time: 3.23s   best: 218.0051\n",
      "Epoch 1724/2000:  train Loss: 165.0782   val Loss: 222.7345   time: 3.23s   best: 218.0051\n",
      "Epoch 1725/2000:  train Loss: 164.4539   val Loss: 221.0207   time: 3.22s   best: 218.0051\n",
      "Epoch 1726/2000:  train Loss: 165.7672   val Loss: 220.1383   time: 3.22s   best: 218.0051\n",
      "Epoch 1727/2000:  train Loss: 164.4623   val Loss: 221.2730   time: 3.27s   best: 218.0051\n",
      "Epoch 1728/2000:  train Loss: 164.9032   val Loss: 221.8652   time: 3.34s   best: 218.0051\n",
      "Epoch 1729/2000:  train Loss: 167.2321   val Loss: 221.5482   time: 3.24s   best: 218.0051\n",
      "Epoch 1730/2000:  train Loss: 165.5160   val Loss: 223.2913   time: 3.23s   best: 218.0051\n",
      "Epoch 1731/2000:  train Loss: 165.5775   val Loss: 221.1792   time: 3.24s   best: 218.0051\n",
      "Epoch 1732/2000:  train Loss: 165.6288   val Loss: 222.6600   time: 3.22s   best: 218.0051\n",
      "Epoch 1733/2000:  train Loss: 164.6708   val Loss: 221.3449   time: 3.22s   best: 218.0051\n",
      "Epoch 1734/2000:  train Loss: 166.5926   val Loss: 222.6564   time: 3.23s   best: 218.0051\n",
      "Epoch 1735/2000:  train Loss: 167.1340   val Loss: 222.2593   time: 3.34s   best: 218.0051\n",
      "Epoch 1736/2000:  train Loss: 164.8570   val Loss: 224.3511   time: 3.26s   best: 218.0051\n",
      "Epoch 1737/2000:  train Loss: 165.4880   val Loss: 223.1093   time: 3.14s   best: 218.0051\n",
      "Epoch 1738/2000:  train Loss: 164.3621   val Loss: 221.8862   time: 3.24s   best: 218.0051\n",
      "Epoch 1739/2000:  train Loss: 165.1206   val Loss: 223.4241   time: 3.51s   best: 218.0051\n",
      "Epoch 1740/2000:  train Loss: 164.4617   val Loss: 222.0759   time: 3.32s   best: 218.0051\n",
      "Epoch 1741/2000:  train Loss: 164.2873   val Loss: 220.8445   time: 3.08s   best: 218.0051\n",
      "Epoch 1742/2000:  train Loss: 165.3571   val Loss: 226.8922   time: 3.15s   best: 218.0051\n",
      "Epoch 1743/2000:  train Loss: 166.0220   val Loss: 221.7352   time: 3.18s   best: 218.0051\n",
      "Epoch 1744/2000:  train Loss: 164.8129   val Loss: 222.4533   time: 3.09s   best: 218.0051\n",
      "Epoch 1745/2000:  train Loss: 165.7322   val Loss: 221.6088   time: 3.09s   best: 218.0051\n",
      "Epoch 1746/2000:  train Loss: 166.2901   val Loss: 222.6171   time: 3.09s   best: 218.0051\n",
      "Epoch 1747/2000:  train Loss: 166.9888   val Loss: 219.9382   time: 3.08s   best: 218.0051\n",
      "Epoch 1748/2000:  train Loss: 163.9250   val Loss: 222.8631   time: 3.08s   best: 218.0051\n",
      "Epoch 1749/2000:  train Loss: 164.3953   val Loss: 221.3662   time: 3.10s   best: 218.0051\n",
      "Epoch 1750/2000:  train Loss: 164.2772   val Loss: 220.8472   time: 3.18s   best: 218.0051\n",
      "Epoch 1751/2000:  train Loss: 164.2840   val Loss: 223.1123   time: 3.90s   best: 218.0051\n",
      "Epoch 1752/2000:  train Loss: 164.2362   val Loss: 222.5235   time: 3.49s   best: 218.0051\n",
      "Epoch 1753/2000:  train Loss: 164.2304   val Loss: 221.3164   time: 3.25s   best: 218.0051\n",
      "Epoch 1754/2000:  train Loss: 164.5154   val Loss: 223.2830   time: 3.23s   best: 218.0051\n",
      "Epoch 1755/2000:  train Loss: 164.7892   val Loss: 222.6551   time: 3.29s   best: 218.0051\n",
      "Epoch 1756/2000:  train Loss: 165.2991   val Loss: 223.8277   time: 3.32s   best: 218.0051\n",
      "Epoch 1757/2000:  train Loss: 165.7739   val Loss: 222.1126   time: 3.23s   best: 218.0051\n",
      "Epoch 1758/2000:  train Loss: 163.9381   val Loss: 220.6402   time: 3.23s   best: 218.0051\n",
      "Epoch 1759/2000:  train Loss: 163.9995   val Loss: 220.9302   time: 3.23s   best: 218.0051\n",
      "Epoch 1760/2000:  train Loss: 164.6635   val Loss: 224.5879   time: 3.22s   best: 218.0051\n",
      "Epoch 1761/2000:  train Loss: 164.3940   val Loss: 223.7913   time: 3.23s   best: 218.0051\n",
      "Epoch 1762/2000:  train Loss: 163.8594   val Loss: 223.1343   time: 3.33s   best: 218.0051\n",
      "Epoch 1763/2000:  train Loss: 163.1743   val Loss: 221.9060   time: 3.29s   best: 218.0051\n",
      "Epoch 1764/2000:  train Loss: 164.2400   val Loss: 220.4059   time: 3.24s   best: 218.0051\n",
      "Epoch 1765/2000:  train Loss: 164.3667   val Loss: 223.5505   time: 3.23s   best: 218.0051\n",
      "Epoch 1766/2000:  train Loss: 164.8956   val Loss: 222.0035   time: 3.23s   best: 218.0051\n",
      "Epoch 1767/2000:  train Loss: 164.4124   val Loss: 221.9155   time: 3.23s   best: 218.0051\n",
      "Epoch 1768/2000:  train Loss: 165.1302   val Loss: 220.9652   time: 3.22s   best: 218.0051\n",
      "Epoch 1769/2000:  train Loss: 164.4293   val Loss: 220.4053   time: 3.23s   best: 218.0051\n",
      "Epoch 1770/2000:  train Loss: 166.4102   val Loss: 226.1638   time: 3.53s   best: 218.0051\n",
      "Epoch 1771/2000:  train Loss: 164.4597   val Loss: 223.2668   time: 3.29s   best: 218.0051\n",
      "Epoch 1772/2000:  train Loss: 165.5507   val Loss: 220.5652   time: 3.10s   best: 218.0051\n",
      "Epoch 1773/2000:  train Loss: 168.2606   val Loss: 219.0654   time: 3.09s   best: 218.0051\n",
      "Epoch 1774/2000:  train Loss: 164.6501   val Loss: 220.3045   time: 3.08s   best: 218.0051\n",
      "Epoch 1775/2000:  train Loss: 163.6959   val Loss: 219.7422   time: 3.08s   best: 218.0051\n",
      "Epoch 1776/2000:  train Loss: 163.7214   val Loss: 220.7455   time: 3.08s   best: 218.0051\n",
      "Epoch 1777/2000:  train Loss: 163.1343   val Loss: 220.3916   time: 3.18s   best: 218.0051\n",
      "Epoch 1778/2000:  train Loss: 167.5191   val Loss: 247.6853   time: 3.14s   best: 218.0051\n",
      "Epoch 1779/2000:  train Loss: 170.0949   val Loss: 220.4511   time: 3.09s   best: 218.0051\n",
      "Epoch 1780/2000:  train Loss: 164.1426   val Loss: 220.4123   time: 3.09s   best: 218.0051\n",
      "Epoch 1781/2000:  train Loss: 162.9874   val Loss: 219.7671   time: 3.09s   best: 218.0051\n",
      "Epoch 1782/2000:  train Loss: 164.0936   val Loss: 220.6165   time: 3.08s   best: 218.0051\n",
      "Epoch 1783/2000:  train Loss: 163.4186   val Loss: 222.3799   time: 3.08s   best: 218.0051\n",
      "Epoch 1784/2000:  train Loss: 164.2049   val Loss: 220.3759   time: 3.12s   best: 218.0051\n",
      "Epoch 1785/2000:  train Loss: 162.9699   val Loss: 223.1790   time: 3.18s   best: 218.0051\n",
      "Epoch 1786/2000:  train Loss: 163.4159   val Loss: 222.0453   time: 3.11s   best: 218.0051\n",
      "Epoch 1787/2000:  train Loss: 163.9766   val Loss: 220.2872   time: 3.10s   best: 218.0051\n",
      "Epoch 1788/2000:  train Loss: 164.2750   val Loss: 221.8298   time: 3.10s   best: 218.0051\n",
      "Epoch 1789/2000:  train Loss: 163.3789   val Loss: 221.4876   time: 3.09s   best: 218.0051\n",
      "Epoch 1790/2000:  train Loss: 163.1877   val Loss: 223.7288   time: 3.08s   best: 218.0051\n",
      "Epoch 1791/2000:  train Loss: 163.2434   val Loss: 221.9358   time: 3.09s   best: 218.0051\n",
      "Epoch 1792/2000:  train Loss: 164.2331   val Loss: 226.1393   time: 3.16s   best: 218.0051\n",
      "Epoch 1793/2000:  train Loss: 164.3769   val Loss: 220.5580   time: 3.16s   best: 218.0051\n",
      "Epoch 1794/2000:  train Loss: 167.9112   val Loss: 223.3934   time: 3.09s   best: 218.0051\n",
      "Epoch 1795/2000:  train Loss: 165.5251   val Loss: 222.3318   time: 3.09s   best: 218.0051\n",
      "Epoch 1796/2000:  train Loss: 164.7774   val Loss: 221.9194   time: 3.09s   best: 218.0051\n",
      "Epoch 1797/2000:  train Loss: 163.0021   val Loss: 222.1339   time: 3.08s   best: 218.0051\n",
      "Epoch 1798/2000:  train Loss: 163.3154   val Loss: 223.5134   time: 3.07s   best: 218.0051\n",
      "Epoch 1799/2000:  train Loss: 163.1843   val Loss: 223.1972   time: 3.12s   best: 218.0051\n",
      "Epoch 1800/2000:  train Loss: 163.6454   val Loss: 222.2214   time: 3.19s   best: 218.0051\n",
      "Epoch 1801/2000:  train Loss: 163.8351   val Loss: 221.8907   time: 3.11s   best: 218.0051\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1802/2000:  train Loss: 162.8265   val Loss: 222.9594   time: 3.10s   best: 218.0051\n",
      "Epoch 1803/2000:  train Loss: 163.5876   val Loss: 221.0533   time: 3.10s   best: 218.0051\n",
      "Epoch 1804/2000:  train Loss: 163.2316   val Loss: 220.3367   time: 3.09s   best: 218.0051\n",
      "Epoch 1805/2000:  train Loss: 163.7925   val Loss: 222.0081   time: 3.09s   best: 218.0051\n",
      "Epoch 1806/2000:  train Loss: 164.6912   val Loss: 220.0632   time: 3.09s   best: 218.0051\n",
      "Epoch 1807/2000:  train Loss: 162.7363   val Loss: 221.0953   time: 3.15s   best: 218.0051\n",
      "Epoch 1808/2000:  train Loss: 164.3318   val Loss: 221.0665   time: 3.17s   best: 218.0051\n",
      "Epoch 1809/2000:  train Loss: 165.1078   val Loss: 222.2804   time: 3.10s   best: 218.0051\n",
      "Epoch 1810/2000:  train Loss: 163.6293   val Loss: 219.9637   time: 3.10s   best: 218.0051\n",
      "Epoch 1811/2000:  train Loss: 162.7008   val Loss: 222.0961   time: 3.10s   best: 218.0051\n",
      "Epoch 1812/2000:  train Loss: 162.7756   val Loss: 222.8580   time: 3.09s   best: 218.0051\n",
      "Epoch 1813/2000:  train Loss: 163.2321   val Loss: 226.4742   time: 3.08s   best: 218.0051\n",
      "Epoch 1814/2000:  train Loss: 165.0478   val Loss: 224.8082   time: 3.09s   best: 218.0051\n",
      "Epoch 1815/2000:  train Loss: 163.5581   val Loss: 226.3083   time: 3.17s   best: 218.0051\n",
      "Epoch 1816/2000:  train Loss: 163.9791   val Loss: 219.4444   time: 3.34s   best: 218.0051\n",
      "Epoch 1817/2000:  train Loss: 163.1544   val Loss: 221.5883   time: 3.40s   best: 218.0051\n",
      "Epoch 1818/2000:  train Loss: 163.3694   val Loss: 222.5936   time: 3.15s   best: 218.0051\n",
      "Epoch 1819/2000:  train Loss: 163.5459   val Loss: 220.3697   time: 3.10s   best: 218.0051\n",
      "Epoch 1820/2000:  train Loss: 162.6751   val Loss: 221.4934   time: 3.09s   best: 218.0051\n",
      "Epoch 1821/2000:  train Loss: 163.1073   val Loss: 220.8497   time: 3.08s   best: 218.0051\n",
      "Epoch 1822/2000:  train Loss: 163.2028   val Loss: 223.2229   time: 3.12s   best: 218.0051\n",
      "Epoch 1823/2000:  train Loss: 162.9977   val Loss: 221.1001   time: 3.19s   best: 218.0051\n",
      "Epoch 1824/2000:  train Loss: 163.0657   val Loss: 224.8441   time: 3.10s   best: 218.0051\n",
      "Epoch 1825/2000:  train Loss: 164.2297   val Loss: 222.1833   time: 3.09s   best: 218.0051\n",
      "Epoch 1826/2000:  train Loss: 163.4369   val Loss: 222.8302   time: 3.11s   best: 218.0051\n",
      "Epoch 1827/2000:  train Loss: 163.2134   val Loss: 226.9619   time: 3.09s   best: 218.0051\n",
      "Epoch 1828/2000:  train Loss: 163.9831   val Loss: 227.0725   time: 3.09s   best: 218.0051\n",
      "Epoch 1829/2000:  train Loss: 163.0617   val Loss: 223.6966   time: 3.08s   best: 218.0051\n",
      "Epoch 1830/2000:  train Loss: 163.0664   val Loss: 222.3249   time: 3.16s   best: 218.0051\n",
      "Epoch 1831/2000:  train Loss: 162.8149   val Loss: 222.9006   time: 3.16s   best: 218.0051\n",
      "Epoch 1832/2000:  train Loss: 162.5131   val Loss: 221.1582   time: 3.10s   best: 218.0051\n",
      "Epoch 1833/2000:  train Loss: 162.3836   val Loss: 223.2371   time: 3.09s   best: 218.0051\n",
      "Epoch 1834/2000:  train Loss: 163.2139   val Loss: 224.1635   time: 3.10s   best: 218.0051\n",
      "Epoch 1835/2000:  train Loss: 163.6971   val Loss: 220.2696   time: 3.09s   best: 218.0051\n",
      "Epoch 1836/2000:  train Loss: 165.8769   val Loss: 223.1255   time: 3.08s   best: 218.0051\n",
      "Epoch 1837/2000:  train Loss: 163.3286   val Loss: 222.6147   time: 3.09s   best: 218.0051\n",
      "Epoch 1838/2000:  train Loss: 162.3480   val Loss: 224.4666   time: 3.18s   best: 218.0051\n",
      "Epoch 1839/2000:  train Loss: 162.6917   val Loss: 222.3248   time: 3.14s   best: 218.0051\n",
      "Epoch 1840/2000:  train Loss: 162.6080   val Loss: 223.9700   time: 3.09s   best: 218.0051\n",
      "Epoch 1841/2000:  train Loss: 162.3703   val Loss: 223.6010   time: 3.10s   best: 218.0051\n",
      "Epoch 1842/2000:  train Loss: 171.8623   val Loss: 220.2672   time: 3.09s   best: 218.0051\n",
      "Epoch 1843/2000:  train Loss: 164.9333   val Loss: 220.2131   time: 3.09s   best: 218.0051\n",
      "Epoch 1844/2000:  train Loss: 162.5106   val Loss: 220.0510   time: 3.10s   best: 218.0051\n",
      "Epoch 1845/2000:  train Loss: 162.5820   val Loss: 221.4616   time: 3.12s   best: 218.0051\n",
      "Epoch 1846/2000:  train Loss: 162.5941   val Loss: 220.3520   time: 3.19s   best: 218.0051\n",
      "Epoch 1847/2000:  train Loss: 162.4293   val Loss: 220.8040   time: 3.09s   best: 218.0051\n",
      "Epoch 1848/2000:  train Loss: 161.8079   val Loss: 222.4654   time: 3.10s   best: 218.0051\n",
      "Epoch 1849/2000:  train Loss: 162.0970   val Loss: 222.0464   time: 3.09s   best: 218.0051\n",
      "Epoch 1850/2000:  train Loss: 162.5344   val Loss: 220.9193   time: 3.08s   best: 218.0051\n",
      "Epoch 1851/2000:  train Loss: 162.9102   val Loss: 221.2474   time: 3.08s   best: 218.0051\n",
      "Epoch 1852/2000:  train Loss: 162.1223   val Loss: 222.8911   time: 3.08s   best: 218.0051\n",
      "Epoch 1853/2000:  train Loss: 162.0905   val Loss: 223.2747   time: 3.16s   best: 218.0051\n",
      "Epoch 1854/2000:  train Loss: 162.1763   val Loss: 223.1191   time: 3.16s   best: 218.0051\n",
      "Epoch 1855/2000:  train Loss: 161.5575   val Loss: 220.6469   time: 3.10s   best: 218.0051\n",
      "Epoch 1856/2000:  train Loss: 162.5372   val Loss: 224.0179   time: 3.10s   best: 218.0051\n",
      "Epoch 1857/2000:  train Loss: 162.7097   val Loss: 223.0408   time: 3.09s   best: 218.0051\n",
      "Epoch 1858/2000:  train Loss: 163.0721   val Loss: 223.7095   time: 3.09s   best: 218.0051\n",
      "Epoch 1859/2000:  train Loss: 162.7232   val Loss: 220.7607   time: 3.10s   best: 218.0051\n",
      "Epoch 1860/2000:  train Loss: 161.6962   val Loss: 222.2318   time: 3.26s   best: 218.0051\n",
      "Epoch 1861/2000:  train Loss: 162.6411   val Loss: 222.0725   time: 3.35s   best: 218.0051\n",
      "Epoch 1862/2000:  train Loss: 162.2514   val Loss: 221.8622   time: 3.26s   best: 218.0051\n",
      "Epoch 1863/2000:  train Loss: 161.9470   val Loss: 223.0280   time: 3.24s   best: 218.0051\n",
      "Epoch 1864/2000:  train Loss: 162.4897   val Loss: 224.0363   time: 3.24s   best: 218.0051\n",
      "Epoch 1865/2000:  train Loss: 161.9209   val Loss: 225.2099   time: 3.23s   best: 218.0051\n",
      "Epoch 1866/2000:  train Loss: 162.1379   val Loss: 220.9697   time: 3.23s   best: 218.0051\n",
      "Epoch 1867/2000:  train Loss: 162.3593   val Loss: 223.7519   time: 3.23s   best: 218.0051\n",
      "Epoch 1868/2000:  train Loss: 162.4167   val Loss: 222.6319   time: 3.35s   best: 218.0051\n",
      "Epoch 1869/2000:  train Loss: 162.6456   val Loss: 220.9234   time: 3.27s   best: 218.0051\n",
      "Epoch 1870/2000:  train Loss: 161.6813   val Loss: 221.6377   time: 3.24s   best: 218.0051\n",
      "Epoch 1871/2000:  train Loss: 162.6236   val Loss: 225.6038   time: 3.24s   best: 218.0051\n",
      "Epoch 1872/2000:  train Loss: 164.3446   val Loss: 221.8307   time: 3.24s   best: 218.0051\n",
      "Epoch 1873/2000:  train Loss: 162.1318   val Loss: 221.0739   time: 3.23s   best: 218.0051\n",
      "Epoch 1874/2000:  train Loss: 161.1532   val Loss: 223.7271   time: 3.22s   best: 218.0051\n",
      "Epoch 1875/2000:  train Loss: 161.7679   val Loss: 223.5072   time: 3.29s   best: 218.0051\n",
      "Epoch 1876/2000:  train Loss: 161.9467   val Loss: 221.0429   time: 3.33s   best: 218.0051\n",
      "Epoch 1877/2000:  train Loss: 161.8223   val Loss: 221.4380   time: 3.24s   best: 218.0051\n",
      "Epoch 1878/2000:  train Loss: 162.1915   val Loss: 222.9917   time: 3.24s   best: 218.0051\n",
      "Epoch 1879/2000:  train Loss: 162.5257   val Loss: 220.9072   time: 3.23s   best: 218.0051\n",
      "Epoch 1880/2000:  train Loss: 161.3560   val Loss: 222.4375   time: 3.22s   best: 218.0051\n",
      "Epoch 1881/2000:  train Loss: 161.3162   val Loss: 222.9246   time: 3.23s   best: 218.0051\n",
      "Epoch 1882/2000:  train Loss: 162.2627   val Loss: 224.2356   time: 3.26s   best: 218.0051\n",
      "Epoch 1883/2000:  train Loss: 161.5236   val Loss: 222.6857   time: 3.34s   best: 218.0051\n",
      "Epoch 1884/2000:  train Loss: 163.1765   val Loss: 221.9111   time: 3.25s   best: 218.0051\n",
      "Epoch 1885/2000:  train Loss: 161.4061   val Loss: 223.0690   time: 3.24s   best: 218.0051\n",
      "Epoch 1886/2000:  train Loss: 161.7592   val Loss: 225.6392   time: 3.23s   best: 218.0051\n",
      "Epoch 1887/2000:  train Loss: 162.0686   val Loss: 224.6157   time: 3.23s   best: 218.0051\n",
      "Epoch 1888/2000:  train Loss: 163.0180   val Loss: 222.5116   time: 3.23s   best: 218.0051\n",
      "Epoch 1889/2000:  train Loss: 161.5390   val Loss: 224.1306   time: 3.23s   best: 218.0051\n",
      "Epoch 1890/2000:  train Loss: 161.5355   val Loss: 220.6482   time: 3.34s   best: 218.0051\n",
      "Epoch 1891/2000:  train Loss: 161.3423   val Loss: 224.5739   time: 3.28s   best: 218.0051\n",
      "Epoch 1892/2000:  train Loss: 162.9970   val Loss: 220.3527   time: 3.23s   best: 218.0051\n",
      "Epoch 1893/2000:  train Loss: 161.6396   val Loss: 219.2974   time: 3.23s   best: 218.0051\n",
      "Epoch 1894/2000:  train Loss: 161.0180   val Loss: 222.6895   time: 3.23s   best: 218.0051\n",
      "Epoch 1895/2000:  train Loss: 161.0171   val Loss: 224.0116   time: 3.23s   best: 218.0051\n",
      "Epoch 1896/2000:  train Loss: 161.3938   val Loss: 219.6783   time: 3.23s   best: 218.0051\n",
      "Epoch 1897/2000:  train Loss: 161.7470   val Loss: 221.3516   time: 3.29s   best: 218.0051\n",
      "Epoch 1898/2000:  train Loss: 161.7550   val Loss: 222.6939   time: 3.32s   best: 218.0051\n",
      "Epoch 1899/2000:  train Loss: 161.6005   val Loss: 224.3996   time: 3.24s   best: 218.0051\n",
      "Epoch 1900/2000:  train Loss: 161.7881   val Loss: 224.3328   time: 3.21s   best: 218.0051\n",
      "Epoch 1901/2000:  train Loss: 161.4516   val Loss: 222.9508   time: 3.10s   best: 218.0051\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1902/2000:  train Loss: 161.6493   val Loss: 222.2278   time: 3.09s   best: 218.0051\n",
      "Epoch 1903/2000:  train Loss: 161.3975   val Loss: 223.9832   time: 3.08s   best: 218.0051\n",
      "Epoch 1904/2000:  train Loss: 160.8046   val Loss: 221.4273   time: 3.11s   best: 218.0051\n",
      "Epoch 1905/2000:  train Loss: 161.1054   val Loss: 221.6856   time: 3.18s   best: 218.0051\n",
      "Epoch 1906/2000:  train Loss: 161.6351   val Loss: 225.5769   time: 3.12s   best: 218.0051\n",
      "Epoch 1907/2000:  train Loss: 161.7411   val Loss: 223.3231   time: 3.10s   best: 218.0051\n",
      "Epoch 1908/2000:  train Loss: 161.9535   val Loss: 223.3342   time: 3.10s   best: 218.0051\n",
      "Epoch 1909/2000:  train Loss: 164.5631   val Loss: 223.1059   time: 3.10s   best: 218.0051\n",
      "Epoch 1910/2000:  train Loss: 160.8021   val Loss: 222.8915   time: 3.09s   best: 218.0051\n",
      "Epoch 1911/2000:  train Loss: 161.0787   val Loss: 222.0775   time: 3.09s   best: 218.0051\n",
      "Epoch 1912/2000:  train Loss: 161.4589   val Loss: 222.7464   time: 3.14s   best: 218.0051\n",
      "Epoch 1913/2000:  train Loss: 160.9148   val Loss: 220.1964   time: 3.19s   best: 218.0051\n",
      "Epoch 1914/2000:  train Loss: 160.8264   val Loss: 220.6183   time: 3.11s   best: 218.0051\n",
      "Epoch 1915/2000:  train Loss: 161.0563   val Loss: 223.7090   time: 3.10s   best: 218.0051\n",
      "Epoch 1916/2000:  train Loss: 160.7895   val Loss: 221.7691   time: 3.10s   best: 218.0051\n",
      "Epoch 1917/2000:  train Loss: 159.8954   val Loss: 223.3374   time: 3.09s   best: 218.0051\n",
      "Epoch 1918/2000:  train Loss: 162.8702   val Loss: 219.1270   time: 3.09s   best: 218.0051\n",
      "Epoch 1919/2000:  train Loss: 161.7164   val Loss: 221.2054   time: 3.09s   best: 218.0051\n",
      "Epoch 1920/2000:  train Loss: 161.9297   val Loss: 219.7753   time: 3.17s   best: 218.0051\n",
      "Epoch 1921/2000:  train Loss: 160.9216   val Loss: 225.9902   time: 3.16s   best: 218.0051\n",
      "Epoch 1922/2000:  train Loss: 161.0598   val Loss: 223.9838   time: 3.11s   best: 218.0051\n",
      "Epoch 1923/2000:  train Loss: 161.5812   val Loss: 223.0738   time: 3.10s   best: 218.0051\n",
      "Epoch 1924/2000:  train Loss: 161.0593   val Loss: 224.0060   time: 3.10s   best: 218.0051\n",
      "Epoch 1925/2000:  train Loss: 161.2593   val Loss: 222.2167   time: 3.09s   best: 218.0051\n",
      "Epoch 1926/2000:  train Loss: 160.5349   val Loss: 221.5075   time: 3.09s   best: 218.0051\n",
      "Epoch 1927/2000:  train Loss: 161.8116   val Loss: 228.8322   time: 3.10s   best: 218.0051\n",
      "Epoch 1928/2000:  train Loss: 162.8118   val Loss: 222.4186   time: 3.20s   best: 218.0051\n",
      "Epoch 1929/2000:  train Loss: 162.0567   val Loss: 222.3826   time: 3.12s   best: 218.0051\n",
      "Epoch 1930/2000:  train Loss: 161.0055   val Loss: 221.3720   time: 3.10s   best: 218.0051\n",
      "Epoch 1931/2000:  train Loss: 160.4096   val Loss: 222.0876   time: 3.10s   best: 218.0051\n",
      "Epoch 1932/2000:  train Loss: 160.4998   val Loss: 221.0334   time: 3.10s   best: 218.0051\n",
      "Epoch 1933/2000:  train Loss: 160.4113   val Loss: 219.8573   time: 3.09s   best: 218.0051\n",
      "Epoch 1934/2000:  train Loss: 160.3781   val Loss: 222.8388   time: 3.08s   best: 218.0051\n",
      "Epoch 1935/2000:  train Loss: 160.0001   val Loss: 224.4554   time: 3.15s   best: 218.0051\n",
      "Epoch 1936/2000:  train Loss: 164.6818   val Loss: 223.4585   time: 3.17s   best: 218.0051\n",
      "Epoch 1937/2000:  train Loss: 161.0972   val Loss: 222.8753   time: 3.11s   best: 218.0051\n",
      "Epoch 1938/2000:  train Loss: 161.1480   val Loss: 221.6957   time: 3.10s   best: 218.0051\n",
      "Epoch 1939/2000:  train Loss: 161.6147   val Loss: 224.0840   time: 3.10s   best: 218.0051\n",
      "Epoch 1940/2000:  train Loss: 162.0361   val Loss: 221.7928   time: 3.12s   best: 218.0051\n",
      "Epoch 1941/2000:  train Loss: 159.7879   val Loss: 222.4658   time: 3.09s   best: 218.0051\n",
      "Epoch 1942/2000:  train Loss: 159.9425   val Loss: 225.8260   time: 3.10s   best: 218.0051\n",
      "Epoch 1943/2000:  train Loss: 160.6082   val Loss: 221.0276   time: 3.54s   best: 218.0051\n",
      "Epoch 1944/2000:  train Loss: 160.1109   val Loss: 222.2646   time: 3.25s   best: 218.0051\n",
      "Epoch 1945/2000:  train Loss: 160.3323   val Loss: 223.9309   time: 3.11s   best: 218.0051\n",
      "Epoch 1946/2000:  train Loss: 161.4596   val Loss: 221.4824   time: 3.10s   best: 218.0051\n",
      "Epoch 1947/2000:  train Loss: 160.2075   val Loss: 222.0311   time: 3.09s   best: 218.0051\n",
      "Epoch 1948/2000:  train Loss: 160.2570   val Loss: 226.0388   time: 3.08s   best: 218.0051\n",
      "Epoch 1949/2000:  train Loss: 160.1920   val Loss: 225.6152   time: 3.08s   best: 218.0051\n",
      "Epoch 1950/2000:  train Loss: 161.8300   val Loss: 223.0973   time: 3.11s   best: 218.0051\n",
      "Epoch 1951/2000:  train Loss: 159.9180   val Loss: 223.8625   time: 3.18s   best: 218.0051\n",
      "Epoch 1952/2000:  train Loss: 160.7313   val Loss: 224.8537   time: 3.12s   best: 218.0051\n",
      "Epoch 1953/2000:  train Loss: 159.5643   val Loss: 227.2935   time: 3.09s   best: 218.0051\n",
      "Epoch 1954/2000:  train Loss: 160.4063   val Loss: 225.3227   time: 3.09s   best: 218.0051\n",
      "Epoch 1955/2000:  train Loss: 160.2069   val Loss: 225.1011   time: 3.09s   best: 218.0051\n",
      "Epoch 1956/2000:  train Loss: 162.0444   val Loss: 224.1708   time: 3.08s   best: 218.0051\n",
      "Epoch 1957/2000:  train Loss: 160.0335   val Loss: 222.8260   time: 3.08s   best: 218.0051\n",
      "Epoch 1958/2000:  train Loss: 159.9663   val Loss: 222.0400   time: 3.14s   best: 218.0051\n",
      "Epoch 1959/2000:  train Loss: 160.5692   val Loss: 222.3459   time: 3.18s   best: 218.0051\n",
      "Epoch 1960/2000:  train Loss: 159.6093   val Loss: 222.7765   time: 3.09s   best: 218.0051\n",
      "Epoch 1961/2000:  train Loss: 160.5590   val Loss: 222.6197   time: 3.09s   best: 218.0051\n",
      "Epoch 1962/2000:  train Loss: 160.2169   val Loss: 224.7212   time: 3.08s   best: 218.0051\n",
      "Epoch 1963/2000:  train Loss: 159.8702   val Loss: 222.7971   time: 3.13s   best: 218.0051\n",
      "Epoch 1964/2000:  train Loss: 160.2031   val Loss: 225.6026   time: 3.09s   best: 218.0051\n",
      "Epoch 1965/2000:  train Loss: 160.4344   val Loss: 224.1252   time: 3.09s   best: 218.0051\n",
      "Epoch 1966/2000:  train Loss: 160.1682   val Loss: 225.0120   time: 3.19s   best: 218.0051\n",
      "Epoch 1967/2000:  train Loss: 160.8337   val Loss: 222.9692   time: 3.15s   best: 218.0051\n",
      "Epoch 1968/2000:  train Loss: 160.0108   val Loss: 219.5152   time: 3.10s   best: 218.0051\n",
      "Epoch 1969/2000:  train Loss: 162.8235   val Loss: 228.3070   time: 3.10s   best: 218.0051\n",
      "Epoch 1970/2000:  train Loss: 161.6557   val Loss: 222.6545   time: 3.10s   best: 218.0051\n",
      "Epoch 1971/2000:  train Loss: 161.7831   val Loss: 224.0270   time: 3.10s   best: 218.0051\n",
      "Epoch 1972/2000:  train Loss: 159.3867   val Loss: 223.4984   time: 3.09s   best: 218.0051\n",
      "Epoch 1973/2000:  train Loss: 159.5379   val Loss: 223.3560   time: 3.13s   best: 218.0051\n",
      "Epoch 1974/2000:  train Loss: 159.4871   val Loss: 222.3282   time: 3.19s   best: 218.0051\n",
      "Epoch 1975/2000:  train Loss: 158.8819   val Loss: 221.9518   time: 3.12s   best: 218.0051\n",
      "Epoch 1976/2000:  train Loss: 159.6785   val Loss: 223.2510   time: 3.10s   best: 218.0051\n",
      "Epoch 1977/2000:  train Loss: 159.2023   val Loss: 225.7954   time: 3.10s   best: 218.0051\n",
      "Epoch 1978/2000:  train Loss: 159.9524   val Loss: 223.9544   time: 3.09s   best: 218.0051\n",
      "Epoch 1979/2000:  train Loss: 159.2394   val Loss: 224.2731   time: 3.09s   best: 218.0051\n",
      "Epoch 1980/2000:  train Loss: 161.1673   val Loss: 222.0365   time: 3.09s   best: 218.0051\n",
      "Epoch 1981/2000:  train Loss: 160.3234   val Loss: 222.9838   time: 3.17s   best: 218.0051\n",
      "Epoch 1982/2000:  train Loss: 159.8385   val Loss: 222.4291   time: 3.15s   best: 218.0051\n",
      "Epoch 1983/2000:  train Loss: 159.4512   val Loss: 220.9713   time: 3.10s   best: 218.0051\n",
      "Epoch 1984/2000:  train Loss: 160.3301   val Loss: 224.9546   time: 3.10s   best: 218.0051\n",
      "Epoch 1985/2000:  train Loss: 160.2785   val Loss: 225.6606   time: 3.09s   best: 218.0051\n",
      "Epoch 1986/2000:  train Loss: 159.2619   val Loss: 222.8382   time: 3.09s   best: 218.0051\n",
      "Epoch 1987/2000:  train Loss: 161.2248   val Loss: 226.3431   time: 3.09s   best: 218.0051\n",
      "Epoch 1988/2000:  train Loss: 160.0050   val Loss: 224.3534   time: 3.11s   best: 218.0051\n",
      "Epoch 1989/2000:  train Loss: 160.6205   val Loss: 223.3387   time: 3.37s   best: 218.0051\n",
      "Epoch 1990/2000:  train Loss: 159.7046   val Loss: 221.3156   time: 3.56s   best: 218.0051\n",
      "Epoch 1991/2000:  train Loss: 159.8641   val Loss: 220.7337   time: 3.14s   best: 218.0051\n",
      "Epoch 1992/2000:  train Loss: 159.0418   val Loss: 224.5665   time: 3.10s   best: 218.0051\n",
      "Epoch 1993/2000:  train Loss: 160.0345   val Loss: 223.8572   time: 3.09s   best: 218.0051\n",
      "Epoch 1994/2000:  train Loss: 159.5128   val Loss: 225.6273   time: 3.08s   best: 218.0051\n",
      "Epoch 1995/2000:  train Loss: 159.8109   val Loss: 222.5813   time: 3.08s   best: 218.0051\n",
      "Epoch 1996/2000:  train Loss: 159.8910   val Loss: 224.7013   time: 3.15s   best: 218.0051\n",
      "Epoch 1997/2000:  train Loss: 159.4829   val Loss: 224.6545   time: 3.17s   best: 218.0051\n",
      "Epoch 1998/2000:  train Loss: 158.7651   val Loss: 224.6109   time: 3.10s   best: 218.0051\n",
      "Epoch 1999/2000:  train Loss: 160.3938   val Loss: 220.6798   time: 3.09s   best: 218.0051\n",
      "Epoch 2000/2000:  train Loss: 159.8685   val Loss: 224.2543   time: 3.09s   best: 218.0051\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete in 104m 55s   best validation loss: 218.0051\n"
     ]
    }
   ],
   "source": [
    "bl_dict={'train':train_bl, 'val':val_bl}\n",
    "log_file = 'log_test1.txt'\n",
    "\n",
    "losses, best_model = train_model(model, criterion, optimizer , max_lr=0.001, dataloader=bl_dict,\n",
    "                                 num_epochs=2000, logFile=log_file, log_every=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'Models/full_1000_0.001_106_13M115_dropout_fused_kBest_0.001_2000_ALL+New_0.2.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load existing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# model.load_state_dict(torch.load('Models/full_1000_0.001_106_13M115_dropout_fused_kBest_0.0006_2000_4.pkl',map_location=train_device))\n",
    "model.load_state_dict(torch.load('Models/full_1000_0.001_106_13M115_dropout_fused_kBest_0.001_2000_ALL+New_0.2.pkl',map_location=train_device))\n",
    "model.to(train_device)\n",
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#accuracy 1st-shot, 2-shots, 3-shots and 5-shots\n",
    "def accuracy(df):\n",
    "    #return np.sum(df[\"prediction\"] == df[\"target\"]) / len(df) * 100\n",
    "    targets = np.array(df['target'].values.tolist()) # 1-dim array (#rows, ) = list\n",
    "    #we went to lists and back to get the last dimension of the (#rows, #size_output) instead of (#rows,)\n",
    "    predictions = np.array(df['prediction'].values.tolist())\n",
    "    \n",
    "    first = np.argmax(targets[:,0:106],-1) == np.argmax(predictions[:,0:106],-1) \n",
    "    second = np.argmax(targets[:,0:106],-1) ==  np.argmax(predictions[:,106:106+106],-1)\n",
    "    third = np.argmax(targets[:,0:106],-1) ==  np.argmax(predictions[:,106*2:106*3],-1)\n",
    "    forth = np.argmax(targets[:,0:106],-1) ==  np.argmax(predictions[:,106*3:106*4],-1 )  \n",
    "    fifth = np.argmax(targets[:,0:106],-1) ==  np.argmax(predictions[:,106*4:106*5],-1)\n",
    "    firstorsecond = np.logical_or(first,second)\n",
    "    firstorsecondorsthird = firstorsecond | third # same as logical or\n",
    "    anywhere = firstorsecondorsthird | forth | fifth\n",
    "    return np.sum(first == True) / len(df) * 100, np.sum(firstorsecond == True) / len(df) * 100, np.sum(firstorsecondorsthird == True) / len(df) * 100, np.sum(anywhere == True) / len(df) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [00:00<00:00, 370.12it/s]\n"
     ]
    }
   ],
   "source": [
    "#get results of the model on the datasets previously loaded\n",
    "val_df = get_results_df(train_val_dataset, val_bl, val_indices, model)\n",
    "test_df = get_results_df(test_dataset, test_bl, test_indices, model)\n",
    "train_df = get_results_df(train_val_dataset, train_bl, train_indices, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test (80.34743309088341, 94.64727067297507, 96.84344325965681, 97.88150554339383)\n"
     ]
    }
   ],
   "source": [
    "#print the accuracies of the model on each dataset.\n",
    "print( \"train\", accuracy(train_df), \"val\", accuracy(val_df), \"test\", accuracy(test_df) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:00<00:00, 354.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of batches 3\n",
      "Data loaded\n",
      "Sizes: (3, 0) batches\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%run utils_k-best.py\n",
    "\n",
    "#single computations 'real' benchmark\n",
    "benchmark_dataset_file = '/data/mm12191/datasets/benchmarks_ds2.json'\n",
    "bench_ds, bench_bl, bench_indices, _, _ = load_merge_data(None, benchmark_dataset_file, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "bench_df = get_results_df(bench_ds, bench_bl, bench_indices, model)\n",
    "\n",
    "#long programs synthetic test set.\n",
    "# benchmark_dataset_file = '/data/mm12191/datasets/dataset_batch190201-190710.pkl'\n",
    "# bench_ds, bench_bl, bench_indices, _, _ = load_merge_data(benchmark_dataset_file, None, 1, filter_func_MC=filter_schedule_MC, filter_func_SC=filter_schedule_SC)\n",
    "# bench_df = get_results_df(bench_ds, bench_bl, bench_indices, model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>exec_time</th>\n",
       "      <th>prediction</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>function_heat2d_LARGE</td>\n",
       "      <td>3.128110</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>function_heat2d_MEDIUM</td>\n",
       "      <td>0.096483</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>function_heat2d_MINI</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>function_heat2d_SMALL</td>\n",
       "      <td>0.010014</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>function_heat2d_XLARGE</td>\n",
       "      <td>20.381001</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>function_jacobi1d_LARGE</td>\n",
       "      <td>5.728760</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>function_jacobi1d_MEDIUM</td>\n",
       "      <td>0.407311</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>function_jacobi1d_MINI</td>\n",
       "      <td>0.004030</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>function_jacobi1d_SMALL</td>\n",
       "      <td>0.040832</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>function_jacobi1d_XLARGE</td>\n",
       "      <td>23.048100</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>function_blur_LARGE</td>\n",
       "      <td>3.213700</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>function_blur_MEDIUM</td>\n",
       "      <td>0.114715</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>function_blur_MINI</td>\n",
       "      <td>0.002760</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>function_blur_SMALL</td>\n",
       "      <td>0.014422</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>function_blur_XLARGE</td>\n",
       "      <td>17.444500</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>function_jacobi2d_LARGE</td>\n",
       "      <td>17424.000000</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>function_jacobi2d_MEDIUM</td>\n",
       "      <td>268.977997</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>function_jacobi2d_MINI</td>\n",
       "      <td>1.246850</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>function_jacobi2d_SMALL</td>\n",
       "      <td>7.210770</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>function_matmul_MEDIUM</td>\n",
       "      <td>33.081902</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>function_matmul_MINI</td>\n",
       "      <td>0.026936</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>function_matmul_SMALL</td>\n",
       "      <td>0.946007</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>function_seidel2d_LARGE</td>\n",
       "      <td>19681.500000</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>function_seidel2d_MEDIUM</td>\n",
       "      <td>232.917999</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>function_seidel2d_MINI</td>\n",
       "      <td>0.835829</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>function_seidel2d_SMALL</td>\n",
       "      <td>7.010360</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>function_heat3d_LARGE</td>\n",
       "      <td>20338.800781</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>function_heat3d_MEDIUM</td>\n",
       "      <td>197.315994</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>function_heat3d_MINI</td>\n",
       "      <td>1.716550</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>function_heat3d_SMALL</td>\n",
       "      <td>21.564301</td>\n",
       "      <td>[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        name     exec_time  \\\n",
       "0      function_heat2d_LARGE      3.128110   \n",
       "1     function_heat2d_MEDIUM      0.096483   \n",
       "2       function_heat2d_MINI      0.001186   \n",
       "3      function_heat2d_SMALL      0.010014   \n",
       "4     function_heat2d_XLARGE     20.381001   \n",
       "5    function_jacobi1d_LARGE      5.728760   \n",
       "6   function_jacobi1d_MEDIUM      0.407311   \n",
       "7     function_jacobi1d_MINI      0.004030   \n",
       "8    function_jacobi1d_SMALL      0.040832   \n",
       "9   function_jacobi1d_XLARGE     23.048100   \n",
       "10       function_blur_LARGE      3.213700   \n",
       "11      function_blur_MEDIUM      0.114715   \n",
       "12        function_blur_MINI      0.002760   \n",
       "13       function_blur_SMALL      0.014422   \n",
       "14      function_blur_XLARGE     17.444500   \n",
       "15   function_jacobi2d_LARGE  17424.000000   \n",
       "16  function_jacobi2d_MEDIUM    268.977997   \n",
       "17    function_jacobi2d_MINI      1.246850   \n",
       "18   function_jacobi2d_SMALL      7.210770   \n",
       "19    function_matmul_MEDIUM     33.081902   \n",
       "20      function_matmul_MINI      0.026936   \n",
       "21     function_matmul_SMALL      0.946007   \n",
       "22   function_seidel2d_LARGE  19681.500000   \n",
       "23  function_seidel2d_MEDIUM    232.917999   \n",
       "24    function_seidel2d_MINI      0.835829   \n",
       "25   function_seidel2d_SMALL      7.010360   \n",
       "26     function_heat3d_LARGE  20338.800781   \n",
       "27    function_heat3d_MEDIUM    197.315994   \n",
       "28      function_heat3d_MINI      1.716550   \n",
       "29     function_heat3d_SMALL     21.564301   \n",
       "\n",
       "                                           prediction  \\\n",
       "0   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "1   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "2   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "3   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "4   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "5   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "6   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "7   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "8   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "9   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "10  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "11  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "12  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "13  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "14  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "15  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "16  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "17  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "18  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "19  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "21  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "22  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "23  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "24  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "25  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "26  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "27  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "28  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "29  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                               target  \n",
       "0   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "1   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "2   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "3   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "4   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "5   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "6   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "7   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "8   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "9   [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "10  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "11  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "12  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "13  [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "14  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "15  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "16  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "17  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "18  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "19  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "20  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "21  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "22  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "23  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "24  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "25  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "26  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "27  [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "28  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  \n",
       "29  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bench_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80.0, 86.66666666666667, 90.0, 90.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accuracy on the chosen benchmark (1st shot, 2-shots, 3-shots and 5-shots (any shot))\n",
    "accuracy(bench_df) "
   ]
  }
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